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Energy savings by energy management systems: A review
Abstract
This study investigated energy saving effects of published papers related to energy management system (EMS), building energy management system (BEMS), industrial, company and factory energy management system (I/C/F/EMS); and EMS for heating, ventilation, air conditioning (HVAC) and refrigerating equipment, artificial lighting systems, motors and others (EMS for equipment). From 1976 to 2014, management performance reported by 305 EMS cases (105 BEMS cases, 103 I/C/F EMS cases and 97 cases of EMS for equipment) is analyzed to evaluate varied energy saving effects. Statistical results show that saving effects of BEMS increased from 11.39% to 16.22% yearly. Inversely, saving effects of I/C/F EMS decreased from 18.89% to 10.35%. Regarding to EMS for equipment, there is no obvious trend but only the averaged saving effect can be reported. EMS for artificial lighting systems has the highest saving effect up to 39.5% in average. For HVAC and other equipment, energy saving effects are around 14.07% and 16.66% respectively. These energy saving performances are correlated with developed EMS functions. The key EMS functions could be identified from their developing progress for effective energy savings. Based on the quantitative analysis, the future trends of EMS are discussed.
... The demand for electricity has become a global concern, especially in light of the climate impacts of generating electric power from fossil fuels [1]. Renewable energy sources have emerged as one of the leading solutions to meet the increasing demand for energy [2]. Solar energy is a key renewable source relied upon to produce electricity through photovoltaic (PV) solar panels [3,4]. ...
... A comprehensive review by Lee and Cheng of 276 papers identified three primary functions that significantly increase BEMS efficiency: scheduling control, tariff and load control, and AI-enabled smart environments. These functions have been shown to enhance energy savings in BEMS from 11.39% to 16.22% [2]. Similarly, Bourhnane et al. investigated various methods for energy consumption prediction and scheduling, highlighting their importance in implementing energy-efficient management systems [17]. ...
... Therefore, it is crucial to identify solutions that detect operational faults and address malfunctioning issues. Lee et al. demonstrated that, on average, 14.1% of the energy consumed by equipment in buildings could be saved if the equipment were operated correctly [6]. ...
... This metric, derived from the classification_report function in sklearn library, is well-suited for FDD applications as it balances precision and recall across all classes, providing a comprehensive measure of performance, particularly when class distributions are uneven. The macro-average F1score is calculated using Eqs.(6) and(7). ...
This study investigates the reliability and robustness of data-driven Fault Detection and Diagnosis (FDD) models for CO2 refrigeration systems (CO2-RS) in supermarkets, focusing on optimal sensor selection and resilience against sensor noise. Using tree-based machine learning algorithms - Random Forest (RF), XGBoost, CatBoost, and LightGBM - we developed FDD models to classify six common faults in a laboratory-scale CO2-RS. The Recursive Feature Addition (RFA) approach identified optimal sensor sets, achieving a 99% F1-score with minimal sensors: four for RF, seven for XGBoost, five for CatBoost, and five for LightGBM. Condenser-side sensors consistently ranked as critical for fault detection. Robustness was assessed by injecting Additive White Gaussian Noise (AWGN) at a signal-to-noise ratio (SNR) of 3 dB into the most important sensor, with XGBoost showing superior resilience at 85.24%, followed by CatBoost (57.07%), LightGBM (49.1%), and RF (49.46%). Sensitivity analysis across high-SNR (10 dB), low-SNR (0 dB), and sensor failure scenarios revealed XGBoost's robustness peaking at 90.23% and retaining 79% under failure, contrasting with sharper declines in other models. These findings highlight a trade-off between sensor count, cost, and reliability, with larger ensembles enhancing noise resilience. This work bridges gaps in FDD literature by integrating sensor optimization with comprehensive robustness analysis, offering a practical framework for improving energy efficiency and fault management in CO2-RS. Future efforts could explore adaptive SNR thresholds and redundant sensor configurations to enhance real-world applicability.
... In [14] presented a grid-integrated system for energy management for smart demand-side energy management. The effectiveness of existing HEMS models was extensively discussed in [12]. In the literature, there has been extensive study of algorithm design for HEMS. ...
... 10 11 You MUST Answer with two first-person sentences for each question. 12 Add an explanation to your answer. ...
Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. This technology helps to reduce energy bills and offers greater demand-side flexibility that supports the power system stability. However, residents who lack a technical background may find it difficult to use HEMSs effectively, because HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs. Recently, Large-Language Models (LLMs) have demonstrated an outstanding ability in language understanding. Motivated by this, we propose an LLM-based interface that interacts with users to understand and parameterize their ``badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS. We further use Reason and Act method (ReAct) and few-shot prompting to enhance the LLM performance. Evaluating the interface performance requires multiple user--LLM interactions. To avoid the efforts in finding volunteer users and reduce the evaluation time, we additionally propose a method that uses another LLM to simulate users with varying expertise, ranging from knowledgeable to non-technical. By comprehensive evaluation, the proposed LLM-based HEMS interface achieves an average parameter retrieval accuracy of 88\%, outperforming benchmark models without ReAct and/or few-shot prompting.
... Organizations can reduce operational costs, enhance productivity, and minimize environmental impact by optimizing energy use. [2]- [5] As describes in [6], the primary objectives of energy management, such as reducing energy waste, improving efficiency, and promoting renewable energy, are achieved through various key techniques. Energy audits provide comprehensive evaluations of consumption patterns, identify-ing inefficiencies and suggesting improvements. ...
Efficient energy management practices are recognized as crucial for optimizing energy utilization. Non-intrusive load monitoring (NILM) has been identified as a promising solution, particularly with the use of deep learning techniques. Conventional NILM models often face difficulties in adapting to changes in power consumption patterns, especially as appliances age. To address this challenge, a self-adaptive NILM model is proposed, which integrates deep learning techniques with transfer learning and pseudo labeling. Unlike traditional NILM models, this approach incorporates a unique self-adaptive feature that enables the model to automatically adapt to changing power patterns resulting from aging appliances. Synthetic data generation and advanced neural network architectures are used for training and validating the model, achieving exceptional accuracy rates in disaggregating power consumption. Electrical appliances used for this experiment are categorized into two groups: on-time fixed devices and on-time variable devices. Experimental results demonstrate the effectiveness of the Self-Adaptive NILM approach with on-time variable devices, such as three-phase refrigerators. The model was tested over a six-year period, focusing on a three-phase refrigerator, and an accuracy rate exceeding 97% in disaggregating power consumption was achieved. It was found that for on-time fixed devices, the conventional NILM model gives better predictions. This high level of accuracy and the findings underscore the potential of this approach for energy management systems. By addressing a significant gap in existing NILM literature, this research introduces the way for the development of more robust, resilient, and adaptive energy management solutions.
... This enables control and optimization across the workflow, improving energy efficiency and reducing costs. As illustrated in Figure 2, prior studies primarily focused on energy management systems such as Building Energy Management Systems (BEMSs) and Energy Management Systems (EMSs), which targeted buildings or large plants [3,4]. Recently, the importance of FEMS within the manufacturing sector has intensified, as detailed energy monitoring and forecasting at the factory level have become achievable. ...
A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption.
... The sourcing of raw materials is a pivotal initial stage in the life cycle of any building project, presenting significant environmental impacts that permeate all subsequent phases-from construction and use to eventual demolition. The process typically begins with the extraction of materials, utilizing heavy machinery for activities such as mining and logging [42]. Embedded carbon emissions and primary emissions from the extraction phase are largely due to the use of heavy machinery in activities such as mining and logging, as well as the transportation of these materials to manufacturing sites. ...
This mini-review addresses the critical problem of significant greenhouse gas (GHG) emissions from the global construction industry, which accounts for 37% of energy-related carbon emissions. With global building areas expected to double by 2060, this paper aims to analyze carbon emission characteristics and control strategies throughout the buildings' entire life cycle, emphasizing the urgent need for effective life cycle carbon management. We introduce and contextualize life cycle assessment (LCA) methods, focusing particularly on Scope 1, 2, and 3 emissions across different life cycle stages of buildings—from design through demolition. Our key findings highlight the potential of intelligent grid energy management systems (EMS) to optimize carbon efficiency in real-time, a pioneering approach that has yet to be widely implemented. The review synthesizes global advancements in green building practices, particularly in regions like Europe, America, and China, and discusses the varied success of these regions in integrating comprehensive carbon management strategies throughout the building life cycle. We conclude with strategic recommendations for future research directions, policy-making, and international cooperation to enhance the sustainability of the construction industry. This study ultimately aims to contribute robust evidence supporting the adoption of advanced LCA methodologies and intelligent EMS in reducing the construction sector's carbon footprint.
Graphical Abstract
... Energy-efficient appliances and fixtures, such as low-flow faucets and Energy Star-rated kitchen equipment, can significantly lower energy consumption in these areas. Implementing energy management systems that monitor and optimize energy use across all amenities can lead to further reductions in energy consumption (Lee & Cheng, 2016). ...
Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70 and 95% of all digital transformation projects fail due to the significant and varied challenges that sports businesses face during the digital transformation process. This is because strategy, not digital technology, drives digital transformation, and without a mature digital transformation model, success is unlikely. This chapter explores the transformative role of Artificial Intelligence (AI) in energy management within sports environments. As modern sports stadiums face increasing energy demands due to advanced technologies and large-scale operations, AI emerges as a pivotal solution for optimizing energy control. The chapter examines how AI tools, such as predictive analytics, machine learning, and real-time monitoring, contribute to energy-efficient practices. These tools enable smart management of lighting, HVAC systems, and other energy-consuming components in stadiums, leading to reduced operational costs and enhanced sustainability. Additionally, AI-driven technologies offer insights into athletes' energy consumption, allowing for tailored training regimes that enhance performance while minimizing waste. Through various case studies, including the integration of AI in major events like the Olympic Games, the chapter highlights the potential for AI to revolutionize energy consumption in sports venues, contributing to a more sustainable and technologically advanced future in sports management.
... Reducing air pollution and the resulting environmental challenges are also of great importance. Energy optimization tools have been widely used in recent years at the global level as a way to achieve the desired answer in the field of reducing energy consumption in buildings (Gopinath et al., 2020), and various studies in this field have been done by (Lee & Cheng, 2016;Nainggolan et al., 2024). ...
Energy management in the construction industry is one of the important issues in the field of construction industry, which can reduce the cost of energy and pollution. Considering that a significant part of the share of energy consumption among the consumer sectors is related to the domestic and commercial sectors and this share continues to increase, the need for research in energy consumption management and the investigation of effective indicators in the construction industry. It seems vital in order to optimize energy consumption. In this research, after examining the society and the statistical sample, data collection has been done to examine effective criteria and options related to energy consumption management. The purpose of this research is to estimate the potential of reducing energy consumption and evaluate its financial and economic benefits in the building through energy audit and identifying strategies to reduce energy consumption. For this purpose, the amount of energy consumption in the typical 6 floor building with 100 square meters of floor area in Jakarta in 2023 was audited using the energy economic evaluation software, and then action strategies to reduce energy consumption have been introduced and their potential to save energy consumption as well as the resulting financial benefits have been evaluated. In the next step, the energy costs and the capital return period were calculated once in the sample building model and again in the case where the energy management system was simulated on that building. The results show that by using energy management solutions (economic construction and facilities), energy consumption is reduced by 3.20%. Also, considering the current price of fuel and the 5-year time period, according to the inflation rate of 2% per year for the return of capital obtained from the economic analysis, additional initial costs will be compensated by applying the proposed policies.
... In numerous existing buildings, the Mechanical, Electrical, and Plumbing (MEP) systems exhibit varying degrees of aging [2], with issues such as reduced energy efficiency of HVAC (Heating, Ventilation, and Air Conditioning) equipment, leaks in the water supply and drainage systems, and aging electrical wiring being particularly prominent. These problems not only affect the functionality of the building but also pose safety risks [3]. ...
Aging buildings pose a significant concern for many large developed cities, and the operation and maintenance (O&M) of mechanical, electrical, and plumbing (MEP) systems becomes critical. Building Information Modeling (BIM) facilitates efficient O&M for MEP. However, these numerous aging buildings were constructed without BIM, making BIM reconstruction a monumental undertaking. This research proposes an automatic approach for generating BIM based on 2D drawings. Semantic segmentation was utilized to identify MEP components in the drawings, trained on a custom-made MEP dataset, achieving an mIoU of 92.18%. Coordinates and dimensions of components were extracted through contour detection and bounding box detection, with pixel-level accuracy. To ensure that the generated components in BIM strictly adhere to the specifications outlined in the drawings, all model types were predefined in Revit by loading families, and an MEP component dictionary was built to match dimensions and model types. This research aims to automatically and efficiently generate BIM for MEP systems from 2D drawings, significantly reducing labor requirements and demonstrating broad application potential in the large-scale O&M of numerous aging buildings.
... The core function of the IoT-enabled EMS is to enable real-time communication and control between various components of the smart energy network. By leveraging IoT technologies, the EMS can gather data from sensors and devices across the grid, allowing for precise monitoring and optimization of energy generation, transmission, and consumption [47]. The EMS is also integrated with user profiling and feedback modules, enabling it to tailor energy control and distribution based on consumers' specific needs and preferences. ...
This chapter explores the transformative integration of the Internet of Things (IoT) with smart grids, revolutionizing how we generate, distribute, and manage electricity. It begins by introducing the fundamental concepts of IoT and smart grids, highlighting their individual characteristics and the potential benefits of their convergence. The chapter then explores the architecture of a smart grid IoT system, including its various components and layers. It discusses specific IoT applications for real-time monitoring of smart grids, leveraging sensors and other IoT devices to monitor grid conditions and detect anomalies. Additionally, it covers remote control and automation in smart grids, highlighting the use of IoT to enable real-time control and automation of grid operations. It also delves into the concept of automated load-shifting strategies, leveraging IoT for demand response programs, which allows utilities to better manage energy demand by incentivizing consumers to reduce their energy usage during peak times. The chapter further examines the diverse applications of IoT in smart grids, including grid analytics, data-driven decision-making, grid monitoring and control, optimization, consumer engagement, security, environmental sustainability, and asset management. Furthermore, it highlights the challenges of implementing IoT in smart grids and suggestive mitigating approaches. The chapter explores the economic implications of IoT-enabled smart grids, addressing pricing models, power costs, tariff calculation, and the emergence of consumer-driven power flows, real-time power trading, and peer-to-peer energy transactions. Finally, it provides a snapshot of the smart grid landscape in India.
... The government should provide green financing to manufacturing firms to reduce their dependency on fossil fuel prices, which can maintain energy price fluctuations [74]. Similarly, training employees on the efficient use of resources, such as energy-saving office equipment, smart lighting, and renewable energy systems, can be beneficial [124]. Also, firms should conduct awareness programs, workshops, and e-learning courses on energy conservation [125]. ...
The manufacturing sector’s carbon emissions and energy consumption is much greater than other counterparts, which needs to be remedied. To solve this issue, energy efficiency is an essential element for sustainable production in the manufacturing process. While a number of studies have examined different energy efficiency policies, no prior study has delved into their interactions. Moreover, there is a lack of studies classifying the policies based on their driving and dependence power. To fill these research gaps, this study identified twelve policies through researching literature, which were further analyzed using the ISM MICMAC approach. Interpretive structural modeling (ISM) was used to develop contextual relationships among identified policies, whereas cross-impact matrix multiplication was applied to classification (MICMAC) to analyze driving and dependence power. The study results reveal that “strategic planning” and “green capabilities” are the most influential policies for energy efficiency, while “green marketing” and “green production” have reduced roles in energy efficiency. The findings of this study can be used to manufacture sustainable goods and services, which can enhance overall corporate sustainability. Businesses can lessen their environmental impact while maintaining their financial sustainability through an energy efficiency scheme.
... AI systems can optimize resource use, thereby minimizing energy consumption and reducing waste 171 . For instance, AIpowered energy management systems can analyze patterns and optimize energy use in real-time, leading to significant reductions in energy consumption and greenhouse gas emissions 172,173 . Similarly, AI can enhance supply chain efficiency, reducing material waste and improving overall resource management 174 . ...
This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-86464-3.
... For example, Intel reduced quality inspections by 20% and increased speed by 15% through data analysis (Chadwick, 2016a). Smart factory systems are also cost-and energy-efficient due to real-time tracking (Chadwick, 2016b;Lee & Cheng, 2016). Cyberphysical systems (CPS) support high output rates and customer satisfaction (Wu et al., (2024) 2019). ...
Concepts like robotic systems, the Internet of Things, 3D printing, and artificial intelligence collectively known as smart systems have revolutionized manufacturing fronts. This research focuses on how the staggered implementations of these technologies affect two vital elements of manufacturing: quality and technological advancement. The implications of these effects are analysed by Blavaan and Bayesian SEM approaches in the study. Based on surveying 33 industrial experts, four research questions are formulated, which were focused on production speed,
resource utilization, labour costs, quality transformation, and technological advancement. The results show that smart factory innovative technologies enhance productivity and efficiency in production. IoT sensors and smart inspection systems
enhance dimensional reliability and material quality, 3D printing, invention cycles, and AI designs.
... This type of maintenance also prevents unexpected breakdowns and allows the life cycle of on-board systems to be extended. Likewise, implementation of an automatic maintenance schedule facilitates the planning process and decision making, allowing the reduction of energy consumption through adoption of planning monitoring schemes (D. Lee & Cheng, 2016). Table 29 presents advantages and disadvantages of planned maintenance from the literature. ...
... Striking a balance between automation and connectivity while minimizing energy consumption is essential. Integration of Energy Management Systems (EMS) equipped with IoT sensors and data analytics enables energy optimization and enhances overall efficiency [78]. Furthermore, the adoption of intelligent energy-efficient technologies and recovery systems can further enhance energy efficiency in smart factory operations [79]. ...
This study investigates the barriers and strategies related to the installation of smart factories in the manufacturing domain, focusing on the manufacturing industry and applying the findings to a German firm to verify the study outcomes. Forty-seven performance variables were evaluated and graded in nine major groups applying the Best Worst Method (BWM), exposing important variables that affect the smart factory’s installation. It resurfaced that connectivity, technological limitations,
and the complexity layer were found to be substantial roadblocks, and the need for an adaptable software platform and a robust IT architecture would be imperative. Nonetheless, it was also disclosed that e-waste and conserving energy were major sources of the obstacles to smart manufacturing adoption. The investigation disclosed that ethical issues were perceived as essential to both societal and employee psychological safety, as well as information governance. Theoretical
implications emphasize the essence of integrating acquired findings by highlighting and prioritizing pertinent obstacles, incorporating feasible solutions, and deepening our awareness of particular issues similar to e-waste and ethical supply chain management. The contributions of business comprise concepts on how to efficiently design and operate smart manufacturing, including a strategic blueprint, comprehensive prioritization, and continuous adaptability.
... This type of maintenance also prevents unexpected breakdowns and allows the life cycle of on-board systems to be extended. Likewise, implementation of an automatic maintenance schedule facilitates the planning process and decision making, allowing the reduction of energy consumption through adoption of planning monitoring schemes (D. Lee & Cheng, 2016). Table 29 presents advantages and disadvantages of planned maintenance from the literature. ...
... Leveraging IoT sensors for on-demand energy management in university premises is a strategic move that not only reduces operational costs but also demonstrates a commitment to environmental responsibility. Energy management is a multifaceted approach that integrates efficiency measures, engineering practices, and process management to achieve cost savings, reduce carbon emissions, and promote sustainability [22] [23]. ...
This review examines the integration of smart management systems in universities through the Internet of Things (IoT), emphasizing its transformative potential to enhance administrative efficiency, improve student engagement, and address critical challenges such as data security and ethical concerns. Using a structured review methodology, we analyzed studies focused on IoT-driven innovations in areas such as energy management, personalized learning environments, and attendance systems. Insights from global case studies, including detailed examples from The Technical University of Cluj-Napoca, Romania, were synthesized to explore the generalizability and applicability of these solutions across diverse institutional contexts. The review followed a systematic approach, selecting studies from reputable academic databases and adhering to predefined criteria for examining IoT integration within university environments. While the findings highlight the significant benefits of IoT for educational management and teaching practices, challenges such as data privacy, system interoperability, and cost barriers remain critical considerations. This comprehensive review aims to guide future research and support the practical implementation of IoT solutions in higher education.
... Consequently, it makes sense to prioritize improving energy efficiency from both a financial and environmental standpoint. Numerous strategies, including optimizing machine utilization, employing energy-efficient technologies and procedures, and implementing advanced energy management systems, may improve energy efficiency in smart factories [6]. ...
Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards. Eight essential criteria are identified, such as the use of renewable energy, the efficiency of production, and the health and safety of workers, to evaluate energy performance. Using the entropy method for criterion weighting and the CRADIS technique for alternative ranking, we prioritize a range of energy-efficient solutions. The novelty of our approach lies in its comprehensive assessment of complex real-world energy management scenarios within smart factories, offering a robust and adaptable decision-support tool. Our empirical results, validated through sensitivity analysis, show that alternative 5 delivers the most significant improvement in energy efficiency. This study provides valuable information for industry practitioners seeking to transition to more sustainable production methods and supports the broader sustainability agenda.
... They balance the energy supply by providing efficient load balancing, cost-saving techniques, and energy • optimizations [41]. These systems are commonly integrated into various home automation software and applications [42] and could be further categorized into building energy management systems (BEMS), company energy management systems (CEMS), industrial energy management systems (IEMS), and factory energy management systems (FEMS) [43]. ...
... Mirrahimi et al. [70] improved the insulation performance of buildings to reduce heating and cooling needs, thereby significantly reducing energy consumption. The installation of relevant energy-saving equipment and energy storage systems can also reduce total energy consumption and dependence on peak energy demand, thereby more stably managing the power grid and more effectively planning energy usage [71]. The international community has also actively responded to energy consumption issues and to the need to reduce greenhouse gas emissions, but the failure of the Paris Agreement [4] has warned the public sector that it is necessary to develop flexible energy policies to quickly adapt to changing weather patterns and mitigate potential negative impacts. ...
The pursuit of carbon neutrality is reshaping global energy systems, making the transition to renewable energy critical for mitigating climate change. However, unstable weather conditions continue to challenge energy consumption stability and grid reliability. This study investigates the effectiveness of various machine learning (ML) models at predicting energy consumption differences and employs the SHapley Additive Explanations (SHAP) interpretability tool to quantify the influence of key weather variables, using five years of data (2017–2022) and 196,776 observations collected across Europe. The dataset consists of hourly weather and energy consumption records, and key variables such as Global Horizontal Irradiance (GHI), sunlight duration, day length, cloud cover, and humidity are identified as critical predictors. The results demonstrate that the Random Forest (RF) model achieves the highest accuracy and stability (R² = 0.92, RMSE = 360.17, MAE = 208.84), outperforming other models in predicting energy consumption differences. Through SHAP analysis, this study demonstrates the profound influence of GHI, which exhibits a correlation coefficient of 0.88 with energy consumption variance. Incorporating advanced data preprocessing and predictor selection techniques remains the RMSE of RF but reduces the RMSE by approximately 25% for the XGBoost model, underlining the importance of selecting appropriate input variables. Hyperparameter tuning further enhances model performance, particularly for less robust algorithms prone to overfitting. The study reveals the complex seasonal and regional effects of weather conditions on energy demands. These findings underscore the effectiveness of ML models at addressing the challenges of complex energy systems and provide valuable insights for policymakers and practitioners to optimize energy management strategies, integrate renewable energy sources, and achieve sustainable development objectives.
... By automating manufacturing schedules to match low electricity prices, EMS technologies lower running expenditures and energy waste. They also assist building managers in reducing energy consumption by controlling HVAC systems, lighting, and other appliances [118], [119], [120]. Smart city initiatives increasingly employ EMS to regulate public transport systems, street lights, and other city-wide infrastructure in large metropolitan areas. ...
Microgrids (MGs) are integral to the evolving global energy landscape, facilitating the integration of renewable energy sources such as solar and wind while enhancing grid stability and resilience. This review presents a comprehensive analysis of control strategies in MG systems, addressing both conventional and advanced methodologies. We explore traditional control methods, such as droop control and Proportional Integral Derivative (PID) controllers, for their simplicity and scalability, but acknowledge their limitations in handling non-linearities and real-time adaptation. Model Predictive Control (MPC), Adaptive Sliding Mode Control (ASMC), and Artificial Neural Networks (ANN) are some of the more advanced techniques that make systems more flexible, better at managing energy, and stable even when operations change quickly. The review further delves into the role of the Internet of Things (IoT), predictive analytics, and real-time monitoring technologies in MGs, emphasizing their importance in enhancing energy efficiency, ensuring real-time control, and improving system security. The review places emphasis on energy management systems (EMS), which optimize supply and demand balance, reduce uncertainty, and enable seamless integration of distributed energy resources (DERs). The paper also highlights emerging trends such as blockchain, AI-driven controls, and deep learning for MG optimization, security, and scalability. Concluding with future research directions, the paper underscores the need for more robust control frameworks, advanced storage technologies, and enhanced cybersecurity measures, ensuring that MGs continue to play a pivotal role in the transition to a decentralized, low-carbon energy future.
... This proportion is expected to rise due to urbanization and economic growth. HVAC systems, responsible for heating, ventilation, and air conditioning, are often inefficient, with around 90% not operating optimally, resulting in substantial energy wastage and financial implications [8]. ...
In the realm of commercial building energy consumption, Heating, Ventilation, and Air
Conditioning (HVAC) systems play a pivotal role, around the globe. However,
inefficiencies in HVAC control practices lead to significant energy wastage through
practices like overcooling, overheating, and running HVAC systems in unoccupied spaces. Compounded by fixed HVAC scheduling due to limited occupancy data, this norm exacerbates energy overuse and ecological impact. This study addresses the challenge through occupancy-based HVAC control strategies, bolstered by machine learning predictions. The study delves into using occupancy insights for HVAC control, utilizing simulations to uncover potential energy savings. It extends its reach into time series forecasting, predicting energy patterns for short terms. This proactive approach empowers HVAC systems to optimize schedules, curbing wasteful consumption and enhancing overall efficiency. This integration of occupancy-based strategies and predictive modeling emerges as a pioneering framework to reduce energy waste in commercial buildings. By coupling real-time occupancy insights with advanced modeling, the research not only reveals untapped energy conservation potential but also charts a path toward sustainable and efficient energy management. In an era of mounting energy efficiency focus, this study promises economic and environmental gains in the commercial building sector.
... The process begins with the collection of physical measures, which are then analyzed by the EMS to enhance decision-making and actions focused on increasing energy efficiency [85]. The EMS consolidates data from several subsystems, including lighting, occupancy, heating, ventilation, air conditioning (HVAC), indoor air quality (IAQ), power management, and security systems [86]. The EMS facilitates real-time evaluation and dynamic regulation of energy parameters via the continuous monitoring of various subsystems, enabling prompt modifications to enhance performance. ...
The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.
... Research on corporate energy management in different sectors suggests that energy audits (EAs) and energy management systems (EnMSs) are necessary but not sufficient for improving energy efficiency in companies (e.g., Rohdin & Thollander, 2006;Rudberg et al., 2013;Backlund & Thollander, 2015;Javied et al., 2015;Bötcher & Müller, 2016;Jovanović & Filipović, 2016;Lee & Cheng, 2016;Schulze et al., 2016;Kluczek & Olzsewski, 2017;Maramon & Casadesús, 2017;McKane et al., 2017;Andersson et al., 2018;Johansson & Thollander, 2018;Schulze et al., 2018;Fuchs et al., 2020;Kabule et al., 2020;Ločmelis et al., 2020;Sola & Mota, 2020). They could help overcome lack of and asymmetric information, adverse selection and some organizational barriers. ...
The EU energy efficiency directive (EED) includes provisions to stimulate increased energy efficiency in companies. Mandatory provisions were first introduced in 2012 and recast in 2023. Policy learning has been suggested as an important route to policy change. This paper analyses how and why policy learning helped revising EU legislation to enhance energy efficiency in companies, using provisions of mandatory energy audits as a case. Negative experience from member states’ governments with the original provisions were voiced shortly after the adoption of EED. A complex process going back and forth between member state and EU levels led by a learning agent facilitated collective learning and change of beliefs, first in member states then in the Council. Several cognitive biases among individuals in the European Commission led to non-learning at the individual level and blocked learning at the collective level. This further blocked policy learning in the EU when EED was amended in 2018. However, external crises and the entering office of a new Commission College in 2019 made the Commission to reconsider its beliefs. Political leadership opened a window for individual and collective learning in the Commission and policy learning in the EU when provisions were changed with the recast of EED in 2023. This suggests that individual and collective learning in the EC is key for policy change to happen. Without new beliefs in the EC, it is hard to get a topic onto the policy agenda. The paper proposes policy recommendations on how to facilitate policy learning and suggests areas for further research.
... Regarding operational costs, Building Energy Management Systems (BEMS) have emerged as crucial tools in optimizing Heating, Ventilation, and Air Conditioning (HVAC) systems. BEMSs enable continuous monitoring and control of building operations, paving the way for intelligent buildings capable of optimizing energy use in real-time [23]. Despite their potential, commercial BEMS predominantly rely on demand-driven control strategies that are often rule-based [24], failing to take advantage of the entire data they generate [25]. ...
Improving energy efficiency in the fruit and vegetable sector helps reduce production costs, environmental impact, and supports food security, especially during periods of high energy price volatility. The sector is energy-intensive, particularly in processing activities like drying, freezing, and canning, which cost about $800 million annually in electricity and fuel. This chapter examines energy consumption patterns and technologies that improve efficiency at different stages of fruit and vegetable production to enhance food security and minimize environmental impacts.
This study reviews the methods used to implement energy management systems (EnMS) in higher education institutions (HEIs) and their impact on improving energy performance considering their relationship with the requirements for an EnMS according to ISO 50001. From 2310 articles, 136 articles and 5 technical reports related to EnMS and energy efficiency were selected and analyzed. A synthesis of the major actions taken by HEIs to enhance their energy performance is presented, including energy management strategies, methods for measuring and estimating consumption, occupant behavior models that influence energy use, barriers to energy efficiency in HEIs buildings, and future challenges. It was found that studies on building energy management systems often do not incorporate an analysis of CO2 emissions reduction. Funding for this research is driven by directives and policies related to energy performance. These results should assist HEIs seeking to implement an EnMS to improve their energy performance and reduce CO2 emissions, thereby contributing to energy security, climate change mitigation, and fostering a new culture of energy use and consumption. It was also found that, although most studies do not explicitly mention the ISO 50001 standard, all of them comply with at least one of its requirements. Additionally, 27% of energy management strategies focus on operational aspects, while 26% involve energy audits, primarily through measurement, estimation, forecasting, energy reviews, and the establishment of an energy baseline (EnBL).
In the context of Industry 4.0, improving energy efficiency in smart factories has emerged as a key priority to drive sustainable industrial growth. However, identifying optimal energy-saving solutions is challenging due to the inherent uncertainty and complexity in decision-making. This study addresses these challenges by proposing a multi-criteria decision-making (MCDM) framework that leverages intuitionistic fuzzy sets (IFSs) to manage ambiguity in the evaluation process. To advance this framework, we develop a suite of novel aggregation operators (AOs), including the intuitionistic fuzzy softmax Dubois-Prade (IFSDP), intuitionistic fuzzy softmax interactive Dubois-Prade weighted average (IFSIDPWA), intuitionistic fuzzy softmax interactive Dubois-Prade ordered weighted average (IFSIDPOWA), and intuitionistic fuzzy softmax interactive Dubois-Prade weighted geometric (IFSIDPWG), which effectively handle uncertainty and vagueness in the criteria assessments. The method based on the removal effects of criteria (MEREC) is utilized to objectively determine the criteria weights, ensuring a robust evaluation structure. For ranking, the alternatives are evaluated through the ranking of alternatives using functional mapping of criteria sub-intervals into a Single Interval (RAFSI) approach. A case study involving eight criteria and five energy-saving solutions demonstrates the framework’s feasibility, with results confirming the effectiveness of our AOs and RAFSI technique in guiding decision-makers toward sustainable energy solutions for smart factories. This framework is poised to support sustainable manufacturing practices in Industry 4.0, fostering greener and more efficient industrial operations.
The aim of the project is to save the energy or power, used in public places like waiting hall, libraries, Class rooms etc. When people not present in the room then the system automatically switched off all the appliances. Whenever any person or group of persons enter in the room then the Counter based on IR Sensor sends signal to Microcontroller, then microcontroller check the light intensity available in that room. Light sensor is used to detect the light intensity of the room. Depending up on the light intensity it decides how many lights need to switch ON. Similarly, it checks the room temperature also. Temperature sensor used here to measure the current room temperature. If room temperature is high and someone present in the room then only fan gets switched on otherwise it remains off, and when all the persons left the room the control unit automatically switched off the devices.
Dynamic Pricing (DP) schemes are becoming a popular solution offered by Utility Grids Companies (UG) to shift the loads to non-peak hours. This strategy supports the reduction of energy consumption in peak hours since the price of power is significantly increased at those hours. Moreover, residential microgrids (MG) can support customers who adopt DP. However, the adaptation of MG with photovoltaic panels (PV) and energy storage systems (ESS) requires advanced techniques to control the power flow among different suppliers (PV, UG, and ESS) and load (including charging ESS). The previous knowledge of how the load behaves and how PV and ESS can interact with this load supports control of power flow. This paper presents a sensitivity analysis of the power consumption and trading of a residential MG in Alabama, considering the DP offered by Alabama Power. Real-time solar insolation and load power consumption data from a local apartment were collected for one-year period. The data is used to simulate different case scenarios and provide a detailed analysis of the savings that the load would have if the simulated MG was installed.
Fault diagnosis (FD) is essential for ensuring the reliable operation of chillers and preventing energy waste. Feature selection (FS) is a critical prerequisite for effective FD. However, current FS methods have two major gaps. First, most approaches rely on single-source ranking information (SSRI) to evaluate features individually, which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI. Second, thermodynamic mechanism features are often overlooked, leading to incomplete initial feature libraries, making it challenging to select optimal features and achieve better diagnostic performance. To address these issues, a robust ensemble FS method based on multi-source ranking information (MSRI) is proposed. By employing an efficient strategy based on maximizing relevance while proper redundancy, the MSRI method fully leverages Mutual Information, Information Gain, Gain Ratio, Gini index, Chi-squared, and Relief-F from both qualitative and quantitative perspectives. Additionally, comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library. From a methodological standpoint, a general framework for constructing the MSRI-based FS method is provided. The proposed method is applied to chiller FD and tested across ten widely-used machine learning models. Thirteen optimized features are selected from the original set of forty-two, achieving an average diagnostic accuracy of 98.40% and an average F-measure above 94.94%, demonstrating the effectiveness and generalizability of the MSRI method. Compared to the SSRI approach, the MSRI method shows superior robustness, with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53% to 6.12%. Moreover, the MSRI method reduced computation time by 98.62% compared to wrapper methods, without sacrificing accuracy.
Due to the reasons of sustainability, energy efficiency becomes a more and more important objective for industrial companies. Among others, the term "energy management" is often mentioned as a practical instrument to raise energy efficiency. Because of the long-term influence at an early stage of the factory life cycle, factory planning provides an important contribution to realize company goals regarding energy efficiency. Until now, there is a lack of methodical concepts considering the combination of energy management and factory planning. In this paper, the interaction between both of these topics is described and by this, the meaning of energy management as an integral part of energy-efficient factories is underlined. Generally, energy management is used in the phase of factory operation to improve the energy performance, including energy efficiency, energy supply security, energy use and energy consumption. Besides, the integration of energy-related tasks in the factory planning process should also be realized as far as possible, e.g. regarding the purchase, distribution, storage and use of energy. This leads to a more systematic and holistic consideration of energy efficiency in organizational and technical processes of a company. (C) 2013 The Authors. Published by Elsevier B.V.
This study was developed in the context of new challenges imposed by the recast of the Energy Performance of Buildings Directive 2010/31/EU (EPBD) and its supplementing regulation. The aim is to find the cost-optimal level for the French single-family building typology, while providing an effective method to deal with a huge number of simulations corresponding to a large number of building configurations. The method combines the use of TRNSYS, dynamic energy simulation software, with GenOpt, Generic Optimization program.
The building that was taken as a reference is a real low-consumption house located in Amberieu-en-Bugey, Rhône-Alpes, France. The model was created and calibrated in TRNSYS and the energy efficiency measures, concerning different technologies for envelope systems and technical systems, were set up as parameters in GenOpt. After a research on the French market, a cost function was created for each parameter and the global cost function (EN15459 Standard) was taken as objective function for the optimization. The particle swarm optimization algorithm was used to minimize the objective function and find the cost-optimal building configuration within the current regulatory framework.
In this study, mobile phones, wearable devices, temperature and human motion detectors are integrated as smart sensors for enabling smart air conditioning control. Smart sensors obtain feedback, especially occupants' information, from mobile phones and wearable devices placed on human body. The information can be used to adjust air conditioners in advance according to humans' intentions, in so-called intention causing control. Experimental results show that the indoor temperature can be controlled accurately with errors of less than ±0.1 °C. Rapid cool down can be achieved within 2 min to the optimized indoor capacity after occupants enter a room. It's also noted that within two-hour operation the total compressor output of the smart air conditioner is 48.4% less than that of the one using On-Off control. The smart air conditioner with wearable devices could detect the human temperature and activity during sleep to determine the sleeping state and adjusting the sleeping function flexibly. The sleeping function optimized by the smart air conditioner with wearable devices could reduce the energy consumption up to 46.9% and keep the human health. The presented smart air conditioner could provide a comfortable environment and achieve the goals of energy conservation and environmental protection.
Switching machinery into standby during non-productive phases for saving energy is rarely applied in today's production environments. Frequent reasons for this are lack of information on potential benefits and the uncertainty on resulting, potentially negative effects. Thus, in a recent project an approach for investigating both economical and ecological benefits was developed, integrating a facile definition process of possible standby modes and basic production system simulation for investigation of different switching strategies. The results of this estimation are evaluated both economically and ecologically, providing a clear decision base for strategy selection. In this paper, the approach is introduced along with exemplary results.
The presented work addresses the topic of energy savings in existing public buildings, when no significant retrofits on building envelope or plants can be done and savings can be achieved by designing intelligent ICT-based service to monitor and control environmental conditions, energy loads and plants operation. At the end of 2010 the European Commission, within the Seventh Framework Program, has founded a project entitled “Smart Energy Efficient Middleware for Public Spaces” (SEEMPubS). To achieve this goal the project will implement, in a set of demonstrator buildings, an interoperable web-based software and hardware solution for real-time monitoring and control of lighting, heating, ventilation and air conditioning services, through both wired and wireless sensor networks. In this paper the first phase of the project, concerning the selection of the environments to be used as demonstrator and the definition of the control and monitoring strategies to reduce energy consumptions for lighting and air conditioning, are presented.
The specific aim of this research paper is to demonstrate how much energy use will be reduced if the simulation -assisted building energy management and control system is applied to a representative large office building. The methodology applied includes: (1) choosing an appropriate building simulation software to assess energy savings, (2) selecting a representative building and then creating the building model for simulation, (3) identifying the year with available weather data and actual building energy consumption data for the building selected, and then calibrating the simulation model using the actual building energy consumption, (4) selecting proper energy-saving strategies and applying them to obtain hourly energy use for a day. The optimal temperature schedule that saves the most energy was identified. Cost effectiveness studies were performed to evaluate how much annual energy saving has been achieved by using the management and control optimization strategy and how long it will pay back.
Occupants’ behaviour is one of the important aspects in diminishing energy waste. It is imperative that working environments should provide comfort to occupants and at the same time, they should also be in line with energy saving practices. The present study aims in investigating energy usage habits of a large enterprise employees in Cyprus, in evaluating their perception on consumption on various energy saving measures and finally, in statistically analyzing their behaviour, attitudes and opinion on energy usage and energy saving measures. Furthermore, the decisions taken by the upper management and its role on energy management is presented and analysed. To achieve the above aims, a detailed questionnaire was designed. Results showed that the majority of employees acknowledge that there is energy waste and that, in theory, they would be open to energy saving measures. They tend to believe that a complete energy management system must be applied, but when they were asked specifically about temperature control, they prefer individual control. Thus, they are not willing to sacrifice their own personal satisfaction for these measures. Furthermore, statistical analysis showed that employees sharing an office and have either less than 10 or more than 20 years of employment are the most dissatisfied about room temperature and thus, they would pose a difficulty in accepting energy saving measures. Finally, the upper management lacks in the promotion of energy saving measures, and this also contributes negatively in the employees’ behaviour.
Constructing a comprehensive energy-saving system is an effective way to meet the development of global lowcarbon economy, energy crisis and the deterioration of ecological environment. In this paper, we made an analysis and evaluation to the practical urgency and constraints of energy-saving system construction with system analysis, combining with our low-carbon economy and the fact of energy saving, we construct a model of energy saving system based on energy, economy and environment in China. Then we put forward some ideas that the way to construct energy-saving system on the perspective of energy supply, energy consumption and the ecological environment based on the model.
Review of literature on the integrated assessment of the environment in buildings and thermal influences on human activity.
Information about the patterns that govern the energy demand and onsite generation can generate significant savings in the range of 15-30% in most cases and thus is essential for the management of commercial building energy systems. Predominantly, heating and cooling in a building as well as the availability of solar and wind energy are directly affected by variables such as temperature, humidity and solar radiation. This makes energy management decision making and planning sensitive to the prevalent and future weather conditions. Research attempts are being made using a variety of statistical or physical algorithms to predict the evolution of the building load or generation in order to optimise the building energy management The response of the building energy system to changes in weather conditions is inherently challenging to predict; nevertheless numerous methods in the literature describe and utilise weather predictions. Such methods are being reviewed in this study and their strengths, weaknesses and applications in commercial buildings at different prediction horizons are discussed. Furthermore, the importance of considering weather forecasting inputs in energy management systems is established by highlighting the dependencies of various building components on weather conditions. The issues of the difficulty in implementation of integrated weather forecasts at commercial building level and the potential added value through energy management optimisation are also addressed. Finally, a novel framework is proposed that utilises a range of weather variable predictions in order to optimise certain commercial building systems.
The excessive use of energy in industrial sectors necessitates the decision maker to always question on how the energy is being used efficiently. Energy used for air-conditioning and lighting in a medium industry counts for almost 60% of the total energy used. The small percentage of energy use reduction relates to the lower product cost and higher profit margins. Therefore, it is important to the decision makers of an industry to have a proper method to audit the building plant and to come up with the practical actions needed in optimizing the use of energy, while at the same time to improve the comfort and product quality. This paper shows the data mining web application for energy audit that can be used in a typical industrial site.
Volunteer computing systems provide an easy mechanism for users who wish to perform large amounts of High Throughput Computing work. However, if the Volunteer Computing system is deployed over a shared set of computers where interactive users can seize back control of the computers this can lead to wasted computational effort and hence wasted energy. Determining on which resource to deploy a particular piece of work, or even to choose not to deploy the work at the current time, is a difficult problem to solve, depending both on the expected free time available on the computers within the Volunteer computing system and the expected runtime of the work – both of which are difficult to determine a-priori. We develop here a Reinforcement Learning approach to solving this problem and demonstrate that it can provide a reduction in energy consumption between 30% and 53% depending on whether we can tolerate an increase in the overheads incurred.
While formal methods to account for uncertainty in process optimization have been developed in the literature, little use of this research work has been reported for industrial applications. An attempt is made in this paper to integrate techniques for modelling and optimization under uncertainty in order to explore flexible operating scenaria and energy management schemes of real industrial utility systems. Multiperiod optimization principles are employed to account for variable demands and/or uncertain operating conditions, while discrete operating decisions (e.g. switching on/off a piece of equipment) are explicitly considered, enhancing, thus, the concept of operating cost for flexible operation.
The transition to renewable energy sources and energy efficiency have become a central topic, also for the producing industry in Romania and all over Europe. Saving energy is on the agenda for companies as well as facilities and public institutions. Energy efficiency in companies can be controlled and systematized in an energy management system. The ISO standard 50001:2011 enables companies and other institutions to achieve a sustainable energy reduction by systematic energy controlling, documentation and raising the awareness of all personnel involved. This paper presents the challenges and benefits of an ISO 50001 implementation in an industrial environment as well as the methodology and systematic approach, but also tools such as energy controlling systems and measurement equipment which are helpful to achieve energetic transparency.
This paper describes a visualization technique to analyze causalities among the productivity indices and energy for improving energy efficiency of factory equipment. Recently, energy-saving has been important worldwide. Especially, energy-saving for factories is very important in manufacturing. Meanwhile, productivity indices must be kept in manufacturing process. Thus, we realize the improvement of energy efficiency on factory equipment by adding our visualization technique to conventional Factory Energy Management System. Our visualization technique quantifies the operational condition of equipment by the energy consumption and the equipment behavior. As the result of the visualization in our factory, our proposed technique could successfully improve the energy efficiency on a molding machine, a press machine and compressors without a negative effect to the productivity that means production volume and supply pressure.
This paper presents an in-service motor monitoring and energy management system. The system is mainly composed of a central supervisory station (CSS) connected by wireless sensor networks (WSN) with DSP devices to sample and process the motor current signals. The motor current signature analysis (MCSA) and spectrum estimation methods are used to estimate the speed, air-torque and efficiency of in-service motors. The motor efficiency performance is evaluated with 6 kinds of line charts. The implementation of the proposed system would help improve motor-driven systems to realize energy saving, economic benefits, and reduce environmental impacts.
The smart electric networks are developed rapidly in our country. Relying on the construction of China State Grid Corporation on smart electricity, to realize the social philosophy as a new lifestyle, like low-carbon, energy saving, environmental protection and so on, this paper designs a home energy management system on web for the full use of intelligent demand side management requirements, the capacity of power grid and its intelligence level. By the use of the control software on WEB, the users can configure or operate the smart home system locally or remotely, get the electricity information and the electrical energy control program. The design plan, process and management effectiveness of the system is only to provide a reference to enhance the level of electricity services and smart electricity construction of the State Grid Corporation.
Energy wastage in buildings is to be minimized to reduce the carbon footprint of electricity. Wireless sensor and actor networks (WSAN) have been providing solutions for effective energy management within buildings. In this paper, we present a decisive server based context aware energy management system for smart buildings through Cyber Physical System (CPS) models. A layered architecture for building energy management is proposed to enhance scalability of the system. Heterogeneous wireless network based multiple radio gateway is proposed and implemented to make the system more adaptive to different applications catering to variable data rates. A smart room test bed is deployed in the IIT Hyderabad campus, where the decisive server collects various physical parameters through sensors, and based on the context generates wireless control messages to power electronics based actuators. Integrating context awareness into the system increases the efficiency in terms of energy savings and was observed to be significant, around 30%. The paper also presents a detailed analysis on the turnaround time required to realise the real saving after recovering investments. Applications are developed to integrate smart phones and tabloids providing web enablement to the end user. In this paper, each of the sensors and actuators in the smart room are associated with a state machine, which enables modelling of the system using Hybrid automata for future scope of applications.
Smart grid and micro-grid are global development trend worldwide in which many countries are planning to do. Some of them will take this as a driving force to the green industry development. They would make use of it for carbon emission reduction. In micro-grid, peak demand response, renewable energy and energy efficiency will also play vital roles as well. We may use the smart meters and building management system to enhance the energy efficiency. In this paper, the author will introduce the implementation ideas of Energy Management Reporting System (EMRS) and Facilities Management Reporting System (FMRS) via the integration of Power Quality, Energy Management System (PQEMS) and Building Management System (BMS). EMRS provides the feedback on the energy management performance and feedback to the daily operation. FMRS reflects the maintenance management and highlight those energy concern repairing items if they fail. It helps to enhance the operational and energy efficiency.
A Home Energy Management System (HEMS) is expected to be vital for saving energy costs considering the time-varying price of electric power in a smart home environment. Studies on various energy resources such as energy storage systems and fuel cells in a smart home environment are required for HEMS development. In the area of the HEMSs, however, there exists very limited research on heating and air conditioning scheduling incorporating customer convenience. This paper presents a smart heating and air conditioning scheduling method for HEMS that considers customer convenience as well as characteristics of thermal appliances in a smart home environment. The prototype software based on the proposed method for HEMS is also implemented.
The energy use in the world is increasing significantly owing to increase in per capita consumption of energy and growing population. Due to increased energy demand and the depletion of existing fossil fuel based sources, it is required to use the energy more efficient. Researches show that, hospitals represent approximately 6% of total energy consumption in the utility buildings sector. Heating, Ventilation and Air Conditioning (HVAC) systems are the major part of electrical energy consumption at the hospitals. In this paper, the research papers and practical studies on energy efficiency and energy saving potentials on HVAC systems at the hospitals are presented. Under the following sections, the latest literatures including research articles, conferences, e-books, handbooks and company reports interested in energy efficiency, energy saving and energy management HVAC systems are summarized. Variant Refrigerant Flow (VRF) technology enables greater energy efficiency and cost savings compared with traditional HVAC systems is also introduced. This detailed review also focuses on the payback periods of some projects on HVAC including the installation of cogeneration, trigeneration, chiller, new burners, heat exchangers and steam trap systems.
In the running process of cloud data center, the idle data nodes will generate a large amount of unnecessary energy consumption. Furthermore, the resource misallocation will also cause a great waste of energy. This paper proposes a three-phase energy-saving strategy named TPES in order to save energy and operational costs for cloud suppliers. The three phases are replica management based on variable replication factor, cluster reconfiguration according to the optimal total costs and state transition based on observed and predicted workloads. These three phases save energy for the system at different levels which enhance the adaptability of our strategy. We evaluate our strategy using the expanded CloudSim toolkit and the results show that the proposed strategy achieves better energy reduction under different conditions in comparison with the existing schemes.
The operation of chiller systems could take up over half of the electricity consumption of air-conditioned buildings in subtropical regions. This paper explains how to examine the effectiveness of their energy management by using data envelopment analysis (DEA). A large-scale system with seven chillers of two different capacities was studied. The scale, technical and overall efficiencies defined in DEA were calculated for each set of operating conditions comprising the system coefficient of performance (COP) and input variables: the compressor power, chiller load and temperatures at the evaporator and condenser sides. Under the existing operating strategy, the average system COP was found to be 5.73 with an average technical efficiency of 0.97. This indicates that the system operated very efficiently for most of the time. Fine-tuning the temperature-related controllable variables could achieve the highest possible COP of 5.83 with technical efficiency of one. The benefits of using technical efficiency to identify energy management opportunities are highlighted.
The emerging of plug-in hybrid vehicles results not only in the increase of electric vehicles as means of transportation, but also in the utilization of vehicle batteries for grid support, which is referred to as vehicle-to-grid (V2G). However, V2G is still at a conceptual stage, and the lack of practical and realistic frameworks to help moving from concept to implementation causes serious challenges to its adoption. In this context, this paper proposes a practical model for the assessment of the contribution of V2G systems as a support to energy management within realistic configurations of small electric energy systems (SEESs) including renewable sources, such as Microgrids. Considering the uncertainty factors related to renewable power sources and gridable vehicles, the model materializes into a robust linear optimization problem suited to be easily integrated in the Energy Management System of SEESs, to support – in operation or operation planning – SEESs’ participation in the electricity market. The paper also presents a practical methodology to model the aggregation of gridable vehicles, contributing to the literature in the field and helping towards the actual implementation of V2G. The efficiency and usefulness of the developed aggregation and optimization models are shown using a realistic SEES case study.
Approximately, 20% of the electricity consumed in the world is spent for lighting. More efficient utilization of the sun, as a natural source of light, for lighting would save electricity used for lighting. The aim of this study is to illuminate a windowless room via a light-pipe and dimmable electronic ballasts. Light-pipe is used for the illumination of the space during the daytime. In case of inadequate daylight, artificial lighting is made via dimmable electronic ballasts and fluorescence lamps. Artificial lighting is supervised by a fuzzy logic control system to keep the illumination level at 350 lux. When there is a motion in the room, the system works with the message of the motion sensor, which, thereby, enables energy saving. Additionally, dimming the lamps result in conversation of the electrical energy used for illumination. After the experimental studies, 350 lux value targeted in the work plane is achieved with ±10 lux error.
Energy and comfort management is the major task for a building automation system. As a trend of next-generation's commercial buildings, intelligent buildings are capable of facilitating intelligent control of the building to fulfill occupants’ needs. Since occupants’ behaviors have a direct impact on the system performance, the building should be able to interact with occupants by responding to their requests and obtaining feedbacks based on their behaviors. In this paper, a multi-agent based intelligent control system is developed for achieving effective energy and comfort management in a building environment. The developed multi-agent system turns out to be capable of facilitating the building to interact with its occupants for realizing user-centered control of buildings.
Although the meaning of energy efficiency is clear, different definitions exist and important issues relating to its implementation still need to be addressed. It is now recognised that complicating factors – such as complex industrial sites and energy flows, multiple products and fuels, and the influence of production rate on energy efficiency – render it necessary to adopt a structured framework to define and measure energy efficiency more precisely. In this paper, a methodology is proposed to build such a framework. The whole energy system of a site is represented using a single matrix equation, which expresses the relationship between imported energies and energy drivers. The elements of the matrix are the specific energy consumptions of each single process. Mathematical process modelling, through statistical analysis of energy consumption data, is used to quantify the specific energy consumption as a function of the output. The results of this structured approach are relevant for energy benchmarking, budgeting and targeting purposes. Furthermore, this approach is suitable for implementation in an energy management system standard (e.g. EN 16001, ISO 50001) or LCA standard (e.g. ISO 14044). Glass and cast iron melting processes are presented in order to illustrate the application of the method.
Energy issues and energy management gain more interest within the society at large as well as amongst companies of different sizes. Yet, even in energy-intensive companies, like process industries, energy management is seldom treated strategically. The purpose of this study is thus to investigate the necessary prerequisites for putting energy management on the strategic agenda in energy-intensive process industries. This is done by the means of a literature review and a case study, and the analysis is based on how energy management is treated from three perspectives; a strategic perspective, an energy system utilization perspective, and an alternative revenue perspective. The case study shows, similar to other process industry companies, that the strategic importance of energy management, to a large extent, is neglected. The research also indicates necessary prerequisites, for each perspective, for highlighting the strategic importance of energy management for a typical company in the process industry sector.
Although integrated building automation systems have become increasingly popular, an integrated system which includes remote control technology to enable real-time monitoring of the energy consumption by energy end-users, as well as optimization functions is required. To respond to this common interest, the main aim of the paper is to present an integrated system for buildings’ energy-efficient automation. The proposed system is based on a prototype software tool for the simulation and optimization of energy consumption in the building sector, enhancing the interactivity of building automation systems. The system can incorporate energy-efficient automation functions for heating, cooling and/or lighting based on recent guidance and decisions of the National Law, energy efficiency requirements of EN 15232 and ISO 50001 Energy Management Standard among others. The presented system was applied to a supermarket building in Greece and focused on the remote control of active systems.
This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as an event driven binary linear programming problem, the output of which specifies the best time to run of smart household appliances, under a virtual power threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. The optimization is performed each time the system is triggered by proper events, in order to tailor the controller action to the real life dynamics of an household. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here overload management, optimization of economic saving in case of Time of Use Tariff and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manufacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.
Despite the industrial sector accounts for about a quarter of total final consumption worldwide and great efforts have been carried out to reduce its energy use in the last decades, there are still substantial opportunities to improve industrial energy efficiency. Among those opportunities, energy management systems (EMSs) are one of the most successful and cost-effective ways to significantly reduce energy use, energy costs and environmental impact without affecting production and quality. This paper describes the development of an energy management system for a naphtha reforming plant by the use of a data mining approach. The paper shows how these techniques have been applied to identify key influence variables on energy consumption and to develop an energy performance model of the plant. Energy baseline and energy targets have been derived for the assessment of achieved and potential energy savings. Plant results show how savings may be achieved after the implementation of the EMS by tracking and adjusting performance against energy targets.
Energy consumption in tea processing in China reached 860,000 tons of coal equivalent in 1988 (2.93 GJ/ton), most of which was provided by biomass. The paper deals with tea production and processing and their energy consumption in 17 provinces, types of tea and associated problems, such as waste of energy, bad ecological effects and the high cost of tea production. The paper also deals with measures to solve the energy problems in tea processing and lists five energy saving techniques in the process.
A multi-microprocessor-based energy management and security system is presented. A number of 1-bit microprocessors (MC 14500) controlling the energy consumption and security system in different locations are connected in a star configuration to an 8-bit general purpose microprocessor (MC 6800). The system proved to be flexible, reliable and cost effective. The system hardware and software is analysed and described. An example of a system interface for an air flow heating system is presented.
This paper discusses why computerized energy management systems have displaced the time clocks and mechanical controllers, and how these new systems perform. A brief review of the development of computerized energy management systems and how they compare with hard-wired digital controllers is presented first. Then the energy management needs of a facility are discussed. Various factors include size of operation, type of equipment and process, and geographical location. Four levels of energy management functions and the corresponding systems are identified. These are: (i) Level 1 system — basic energy management, (ii) Level 2 system — advanced energy management, (iii) Level 3 system — total energy management, and (iv) Level 4 system — total engineering applications. The hardware used in these systems range from simple demand controllers to minicomputers.The evaluation criteria for energy management systems (EMS) are discussed. A classification of EMS hardware and features is presented. This also includes discussion on the plant size and type, computer facility, number of control points and features.
Fast and controlled regulation of voltage and reactive power may be obtained in large industrial installations which include repetitive or frequent start-up of induction motors by using static VAR compensators (SVCs) to control start-up time and inrush transient current and to maintain consumed and reactive power at minimum levels. Consequently, reduced energy losses and kW generation savings are expected. For a typical industrial load, these savings are evaluated quantitatively and are proved to be sufficient to justify the application of SVCs for energy saving purposes, accompanied by whole system performance improvements. Matching of reactive power demand during normal running conditions contributes to a decrease in energy loss and to increases in motor currents and torques.
The residential air-conditioning load is a significant component of electric utility peak demand, which typically occurs on very hot summer afternoons. Efforts by utilities to shave or shift air-conditioning demand to off-peak periods in the day have been spurred by low-cost electronics and include such strategies as direct-load control and price-induced local control by homeowners. We propose alternative practical strategies of peak shaving that use the opportunities offered by modern electronics as well as a more intelligent use of the thermal mass storage inherent in the structure and furnishings of the house. Using the framework of a simplified electrical analogue, we predict the thermal performance of the residence when the air-conditioner is switched off and illustrate the validity of such simplified estimates with monitored data from an actual residence. Finally, we discuss practical aspects related to the implementation of these strategies, particularly as to what is needed in terms of electronic sophistication of the thermostat.
The potential for energy conservation via the implementation of proven technology within United Kingdom industry is examined using a disaggregated model of individual industrial sectors, fuels supplied, and end-use energy purposes. Energy savings are quantified following the application of general and process-specific conservation measures. Overall, a 30% reduction in energy supply may be achieved at present output levels, varying from 22% in the Paper, Printing, and Stationery sector to 35% in the Food, Drink, and Tobacco inductries. Additionally, a pricing regime in favour of solid fuels is expected to effect a shift away from oil- and natural-gas consumption towards coal. With the advent of low-energy-intensive industries increasingly replacing traditionally high-energy-consuming operations such as iron and steel making, shipbuilding, and heavy engineering, there appears to be no reason for supposing that the continuing overall decline in the energy intensity of British industry will come to a halt. Industrial growth does not require profligate energy consumption.
The ambient temperatures in Kuwait have a direct impact on the total national electrical power demand pattern. Electrical energy is primarily required for comfort air-conditioning. The large-scale introduction of cool storage for energy conservation and improved energy management will reduce the needed electricity generation capacity.
Computers are becoming more commonly used by farmers for improving their management. The big need is ‘user friendly’ software for use by farmers. An integrated water-energy management system was developed. It includes ‘user friendly’ software run on a microcomputer which communicates to center pivot irrigation systems via radio. The program provides monitoring, control, irrigation scheduling and electrical load control. The system has been used and accepted by our cooperator for three irrigation seasons. He has been able to improve his management and reduce the amount of overirrigation and the variation between systems of total water applied.
Several types of lighting control strategies, techniques, and equipment are examined with respect to cost and performance. Daylighting is found to require the use of sophisticated equipment that can provide more than one control strategy. Simple control systems can reduce the lighting load by 12 to 50%, while implementing four control strategies provides savings from 60 to 79%. The use of daylighting, properly integrated with electrical lighting, makes economic sense and will be a more common practice in the future.
High energy consumption and unsatisfactory environmental quality is a long-standing problem of metallurgical enterprises. In order to reduce consumption and improve environmental quality, information technology can be used as a platform and integrating new technologies, techniques and management measures to construct the security, stability, economic and efficient energy supply system for metallurgical enterprises. An energy monitoring and management system of Linyun Iron and Steel Group is designed base on the fieldbus, GPRS, ORACLE database and expert system technology, and the problems in realization of the system is also be discussed in this paper.
In recent years, there has been a dramatic increase in energy consumption in Saudi Arabia. The building sector being the largest consumer of electric energy represents a major potential contributor for reducing energy consumption. Due to their functional and operational characteristics, commercial buildings relatively consume more energy (per unit area) than other types of buildings. The heating, ventilating and air-conditioning system (HVAC) is one of the largest end-users of energy in these buildings, particularly in harsh climates. Energy efficient design and operation of HVAC systems in commercial buildings can offer major opportunities for reduced energy consumption and contribute to sustainable development. However, improper utilization of energy conservation measures can result in reduced environmental quality. This in turn exposes the occupants to thermal discomfort and health risks, and consequently diminishes the economic value of the facility. Therefore, a well assessed and balanced energy conservation strategy is required to achieve energy efficiency while maintaining desired level of thermal comfort. In this study, major design and operational parameters for different types of HVAC systems influencing energy consumption are investigated utilizing the Visual-DOE program. Results indicate that energy savings of up to 30% can be obtained while maintaining acceptable level of thermal comfort when HVAC systems are properly selected and operated.