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Energy savings by energy management systems: A review

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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.

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... For example, although the review was not specifically focused on industrial applications, no reviews were found on industrial objects. The closest to the production sector was Ref. [10] which focused on small and medium-sized enterprises and Ref. [11] on energy management systems also for industrial applications. Overall, the results indicate opportunities for reviews focused on specific building types, such as offices, schools, and hospitals. ...
... A citation network analysis of EE renovations [99] found that research activities centred on thermal insulation and retrofitting space heating systems, the focus being thermal insulation rather than space heating. In a review of green retrofit [38] the frequency of retrofit design parameters was highest for building material (26), followed by opening/window (18), roof (14), and HVAC systems (11). In a review of the performance optimisation of existing buildings [50], most of the EE measures were related to insulation (87) and windows (82) compared to HVAC systems (78) and renewable energy sources (43). ...
... In a simulation-based comparison of intelligent control approaches for residential heating systems [82], automated setpoint variation provided median energy savings of 21-26% compared to on-off control and higher thermal comfort compared to programmable thermostats. An evidence-based review on energy savings by energy management systems [11] found energy saving effects for HVAC of around 14.1%. The highest energy saving ratio of 46.9% was achieved by feedforward human intention and interaction with users through smart phone to optimise the schedule. ...
Article
Heating, ventilation and air conditioning (HVAC) and domestic hot water (DHW) systems are the main end-use energy consumers in buildings. Simultaneously, building retrofitting plays a key role in reducing energy consumption and environmental burdens. Therefore, this study aimed to identify the main research trends and possible developments in the interrelated fields of HVAC&DHW systems and building retrofitting. To this end, we searched scientific databases for related review studies. The search yielded 100 eligible articles; however, only 13 of them fully focused on HVAC&DHW in building retrofitting, indicating considerable potential for further reviews. The retrofit measures in the reviews were dominated by space heating, heat/cool generation, and control systems. Heat pumps were the most frequent retrofit measure, quoted in 42 of the reviews, followed by upgrading the control system (41). Energy efficiency was the focus of 61 of the reviews, followed by energy modelling (13), indoor environment quality (11), and life cycle assessment (8). The research suggestions found in the reviews were grouped into four main topics: reducing uncertainty in energy savings, developing calculation tools for building retrofit, more complex retrofit evaluation, and digitisation. For specific technologies, the potential was mainly related to adapting HVAC&DHW technologies to retrofit and integrating renewable energy sources into existing buildings. Research was also lacking for specific regions and building types. The results of this review help professionals better focus their efforts in the research and review activities of heating, ventilation and air conditioning, and hot water systems, especially in the context of building retrofitting.
... EM is a generic term for all societal applications. It includes planning and operating energy production and consumption units [15] . When renewable energy sources are used, EM covers energy distribution and storage. ...
... Theories and practices for EM, efficiency, and savings have been used for some time [5][6][7][15][16][17] . Several studies have been related to the broad issue of EM and efficiency. ...
... Since then, FCM has been used to model complex systems. A detailed presentation of FCM was described previously [15][16] . FCM investigates complex situations and deals with fuzzy or uncertain environments through reasoning [36,37] . ...
Article
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Soft computing, especially fuzzy cognitive maps (FCMs), has become increasingly applicable to energy management and policy-making. In recent decades, there has been a worldwide effort to minimize energy consumption and manage energy flow in private and public buildings. We present a critical overview of today’s applications of FCM-based methods in the energy domain. We analyzed FCM methods related to energy planning, efficiency, sustainability, transition, forecasting, energy policy, and scenario analysis. We highlight FCM's applicability in the energy domain, especially its contribution to the academic and research communities. Specific drawbacks and limitations were identified while using FCM methods on several challenging applications, primarily when learning algorithms are used. A new approach addressing these issues is provided and defined as the advanced fuzzy cognitive maps (AFCM) approach. These drawbacks are considered when providing future research challenges of FCMs for building energy management and efficiency. Finally, research gaps are identified, and we suggest solutions, especially AFCM is advanced fuzzy cognitive maps.
... Electrical networks are transforming their traditional ways of operating as well as their main generators and loads into more dynamic configurations that favor the intelligent interaction of their devices to improve their technical, financial, and environmental conditions [1]. These transformations are aimed at optimizing the use of the existing energy resources and improving the quality of life of the users Maintenance cost per kW of the photovoltaic generator located at node . ...
... Cost of purchasing or producing energy from the conventional generator located at node at hour ℎ. 1 Objective function in charge of optimizing the microgrid operating costs using batteries. 2 Objective function in charge of optimizing the microgrid energy losses. ...
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This paper addresses the problem of managing battery energy in urban and rural alternating current networks, aiming at improving their financial, technical, and environmental indicators. To this end, a mathematical model was formulated that proposes as objective functions the optimization of energy operational costs of the grid, the minimization of power losses associated with energy transport, and the reduction in CO2 emissions related to the production of energy. This model also considers the set of constraints involved in the operation of an alternating current network in an environment with distributed energy resources (Photovoltaic generators + Batteries storage systems). A master–slave strategy that combines a parallel version of the Vortex Search Algorithm (VSA) and an Hourly Power Flow method based on Successive Approximations (HPFSA) was proposed as the solution methodology. To create the test scenarios, urban and rural electrical networks documented in the specialized literature were adapted using generation and demand data of an average day of operation in Medellín-Antioquia (Urban) and Capurganá-Chocó (Rural). These data represent the energy behavior of an urban and a rural network within the Colombian territory. In relation to the distributed energy devices, the integration of three Photovoltaic Distributed Generators (PV DGs) and three lithium-ion batteries of different types into the electrical networks was considered. In addition, the energy production costs and CO2 emissions of the local network and the diesel fuel in Colombia were determined, as well as the maintenance costs associated with the batteries and the PV DGs. With the purpose of validating the effectiveness of the proposed method in terms of solution, repeatability, and processing times, two comparison methods reported in the specialized literature were employed to solve the problem addressed in this paper. In the urban and rural networks under study, the proposed solution methodology achieved the best results in terms of solution quality, repeatability, and processing time.
... A general conclusion is that energy audits are necessary but not sufficient for improving energy efficiency in industry. Some research has also been undertaken on the role and effects of (certified) energy management systems in industry [6,[28][29][30][31][32]. Implementation of energy management systems can contribute to energy savings, increase the adoption of low carbon practices, and improvements of carbon and economic performance of companies [33][34][35]. ...
... Some research has also been undertaken on the role and effects of (certified) energy management systems in industry [6,[28][29][30][31][32]. Implementation of energy management systems can contribute to energy savings, increase the adoption of low carbon practices, and improvements of carbon and economic performance of companies [33][34][35]. Lee and Cheng [29], on the other hand, found, in their literature review of effects of energy management systems on energy savings, that energy savings in industry decreased after implementation of energy management systems. Jovanović and Filipović [36] claimed that certified (ISO 50001) energy management standards represent a good practice of energy management in industry, but they are not the best models to improve energy efficiency. ...
Article
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This paper applied the advocacy coalition framework to explore and explain the political processes creating policies to enhance energy efficiency of European Union (EU) industry. The paper used legislation on energy audits and energy management systems as a proxy for EU policy on energy efficiency in industry. Based on qualitative text analysis of EU policy documents, including a proposal to recast the energy efficiency directive, amendments to the proposal suggested by Member States, the Council and the European Parliament, and reports from negotiations, the paper identified four advocacy coalitions with different core beliefs, spanning from those that want few companies to implement energy audits or energy management systems, and that recommendations from audits should not be mandatory to implement, to those that advocate that many companies implement energy audits and management systems and that it should be mandatory to implement measures recommended in audits. It was further found that policy change followed an external shock, deliberative negotiations, and policy-oriented learning. The identification of core beliefs and advocacy coalitions will help policymakers and other stakeholders become more aware of their own and others’ values on energy efficiency and how these could be changed. As important was the differentiation of deep core beliefs, policy core beliefs and secondary beliefs. Which beliefs can be easily changed, which cannot?
... In order to realize net-zero-energy building, it is essential to reduce energy use and optimize energy usage efficiency. Accordingly, the need for a building management system (BMS) emerged [8]. The BMS includes the building automation system (BAS), which monitors the status of facilities and automatically controls the operation, and the energy management system (EMS), which provides optimized energy management measures while maintaining a comfortable indoor environment. ...
... The BMS includes the building automation system (BAS), which monitors the status of facilities and automatically controls the operation, and the energy management system (EMS), which provides optimized energy management measures while maintaining a comfortable indoor environment. Using EMS can save about 16% of the annual energy used in buildings [8]. Several preconditions must precede to operate the EMS stably. ...
Article
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In this study, prediction performances of a regression model and deep learning-based predictive models were comparatively analyzed for the prediction of hourly insolation in regions located at the temperate climate and microthermal climate with high precipitation. Unlike linear regression models, artificial neural networks (ANN) and long short-term memory- (LSTM-) based models achieved reliable predictive performances with CV(RMSE) of 14.0% and 15.8%, respectively. This study proposed the direction of future research by improving the performance of predicting insolation at 1 hour after the current time-step, which has time-dependent characteristics, by utilizing insolation at 24 hours before the current time-step and insolation at the current time-step in addition to the forecasted weather data. In the proposed models, a large error occurred at sunrise and sunset times, suggesting the possibility of improving predictive performance by utilizing variables related to sunrise and sunset in the future. Along with Cheongju, the proposed model could properly predict the hourly insolation in other regions around the world. The results of predicting other regions derived slightly higher prediction errors than Cheongju. However, it is expected that it will be possible to predict the hourly insolation in other regions with better prediction performance if variables related to geographical location are additionally considered in the future.
... A review of the energy saving achieved by energy management systems is presented in [10]. This comprehensive survey investigates 276 papers related to EMSs in buildings, in the industrial environment, and for specific equipment. ...
... Most of the EMSs presented in the literature [10,[17][18][19] focus on reducing energy consumption from the primary grid. The system proposed in this paper aims to be autonomous, without connection to the primary grid. ...
Article
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This paper proposes an intelligent energy management system based on multiple renewable energy sources. The intelligent energy management system is defined as a flexible energy management system built by integrating multiple renewable energy sources and facilities for energy storage. The general objective of this paper is to propose a solution to increase the use of energy potential from renewable sources by embedding small-sized energy sources to behave as a higher-power energy source. The proposed system includes solar, wind, and hydro as renewable sources. As the system is not connected to the primary distribution grid, two alternatives for energy storage are also included: batteries and a water basin used for hydro energy. The system includes a diesel generator as a reserve. The system can be adjusted depending on the consumers it serves and the location where it is implemented (i.e., the potential of electricity, wind, and hydro). The main contribution of this paper is the use of an energy storage concept in the form of a “natural” battery system composed of a water storage basin into which water is pumped when we have a surplus of energy from renewable generators.
... Several DSM strategies can be proposed. Such as forecasting of residential energy, using direct load control [10], using optimization and artificial intelligence [11], incentive-based demand response program [12], load shifting [13], energy management systems (EMS) [14], photovoltaic systems (PV) integration [15]. ...
... Regarding the previous study that proposed the TOU structure in Malaysia [14], the authors have designed multiple time zones TOU tariff structures to benefit residential consumers. For TOU operation, predetermined rates for specific days or weeks. ...
Article
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A price-based program through a time of use tariff (TOU) program is one of the initiatives to offer sufficient benefit for both consumers and generations sides. However, without any strategy for implementing optimal load management, a new tariff design structure will lead to the miss perception by electricity consumers. Therefore, this study offers an investigation toward appropriate TOU tariff design to reflect load profiles. Concurrently, the ant colony optimization (ACO) algorithm was proposed to deal with the load shifting strategy to determine the best load profiles and reducing the consumers' electricity cost. The sample load profiles data is obtained from various residential houses, such as single-story, double-story, semi-D, apartment, and bungalow houses. The significant comparison between baseline flat tariffs to several TOU tariffs has shown an improvement in the percentage of cost saving for approximately 7 to 40%. Furthermore, the identified load management was observed where the maximum load shifting weightage was set up to 30% to reflect the consumers' effort towards energy efficiency (EE) program. The previously proposed TOU design was identified to be a suitable structure that can promote balancing of EE and demand response (DR) program effort in most consumers' houses category in Malaysia.
... Several studies have addressed various issues related to microgrid energy management, with a focus on HEMS as an energy transactions optimizer. Lee et al. [10] conducted an analysis of EMS literature from 1976 to 2014 and found that future HEMS should prioritize advanced human interaction and feedforward control for energy savings and comfort. Additionally, these systems should efficiently integrate the smart grid paradigm to balance local energy storage, production, and use grid to prevent resource instabilities. ...
... Building energy consumption is a complex system whereby various influencing factors interact with each other, and even small changes can have a significant impact [48]. Due to the relatively diverse volumes and forms of office buildings, the building energy-saving rate (ESR) and renewable energy utilization rate (REP) are used as an evaluation index. ...
Article
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In the current context of huge global energy consumption and harsh climatic conditions, the energy efficiency and sustainability of buildings have received much attention. The nearly zero-energy building (nZEB) is a feasible solution for solving the energy crisis in the building sector in recent years, and it is important to study the adaptability of its technology system. However, existing studies have not addressed well the issue of the impact of complex and diverse climates on the technology systems of nZEBs. Secondly, in contrast to residential buildings, nearly zero-energy technology systems for office buildings need to be further developed. This study takes the hot summer and cold winter (HSCW) zone of China as an example and uses numerical simulations and orthogonal experiments to investigate the adaptability of nearly zero-energy office building technology systems under complex and diverse climate conditions. The results show the following: (1) Passive technologies are greatly affected by the complexity and diversity of climates. Optimal envelope thermal parameters tailored to specific zones are identified. Specifically, the optimal level of KWALL in the CT and HSCWC zones is 0.2 W/(m2·K), and the optimal level of KWALL in the HSWWT zone is 0.3 W/(m2·K); the optimal level of KROOF in the CT zone is 0.15 W/(m2·K), and the optimal level of KROOF in the HSCWC and HSWWT zones is 0.25 W/(m2·K); (2) Active technologies do not mainly receive the influence of the complexity and diversity of climates, and ED, HR, and TS measures should be adopted for office buildings; (3) The rational utilization of renewable energy is influenced by local resource conditions. This study evaluates the adaptability of GSHP, ASHP, and BIPV technologies. To better meet the requirements of nearly zero-energy office buildings, it is recommended to adopt GSHP for the CT zone and ASHP for the HCWWT zone. This study will be helpful for the development of nearly zero-energy office building technology systems in other complex and diverse climatic zones.
... Se han realizado diferentes estudios de revisión sobre BEMS. Por ejemplo, en [7] se investigan diferentes estrategias de gestión en edificios residenciales y no residenciales, en [8] se realiza un estudio de revisión de las tecnologías disponibles, en especial la comunicación por línea eléctrica (PLC, por sus siglas en inglés) para la comunicación BEMS, en [9] se realiza un estudio de revisión que trata sobre los efectos de ahorro de energía relacionados con BEMS y en [10] se enfatizan los principales desafíos para la implementación a gran escala de los BEMS. ...
... Solar radiation prediction is an important factor as its impact on living matter and feasible applications for many productive purposes such as renewable energy (Lee and Cheng 2016;Mohanty et al. 2016), direct or indirect conversion of sunlight into electricity (Saberian et al. 2014;Okoye and Solyalı 2017;Lalwani et al. 2011) and heating systems for water (Kalogirou et al. 1999) or air (Karim and Hawlader 2004). Solar radiation on Earth's surface plays a vital role in many fields, including meteorology [8,9], irrigation (Gao et al. 2013;Twersky and Fischbach 1978;Hernandez-Ramirez et al. 2014), solar energy (Yeh and Lin 1996;Bilal et al. 2012;Cruz-Peragon et al. 2012;Bataineh and Dalalah 2012;Lv et al. 2018) and drought monitoring (Zhang et al. 2019). ...
Article
Accurate estimation of solar radiation is crucial for harnessing this abundant natural resource effectively. Measuring solar radiation directly requires ground station networks, which are either unavailable or very limited in many regions of the world, including Vietnam, particularly in remote areas due to resource constraints. Therefore, this study was carried out with the objective to develop hybrid artificial intelligence (AI) models to predict solar radiations correctly using other meteorological data such as wind speed, relative humidity, maximum and minimum temperature and rainfall which can be measured at site easily. In this study, we have proposed three novel hybrid AI models, namely ANFIS-GA, ANFIS-BBO and ANFIS-SA, which combine the adaptive neuro-fuzzy inference system (ANFIS) technique with genetic algorithm (GA), biogeography base optimization (BBO) and simulated annealing (SA), respectively, for predicting daily solar radiation in Hoa Binh province , Vietnam. The performance of these hybrid models was evaluated using statistical indicators, including correlation coefficient (R), root-mean-squared error (RMSE) and mean absolute error (MAE). The results demonstrate that all three optimized models outperform the single ANFIS model. Among them, the ANFIS-BBO model exhibits the highest predictive capability (RMSE = 3.141 MJ/m 2 , MAE = 2.439 MJ/m 2 , R = 0.874). Sensitivity analysis reveals that maximum temperature is the most influential factor for predicting daily solar radiation. The findings of this study have significant implications for predicting solar radiation using AI methods, particularly ANFIS-BBO, with minimal meteorological data in remote locations not only in Vietnam but also globally.
... -MPC demonstrated the highest efficiency for energy saving [184] Development of hybrid training methods for short-term load forecasting -fuzzy ANN, ANN with supportvector machine (SVM), ANN with GA, wavelet-based NN, and ANN with gradient-based learning techniques improve accuracy of hourly load forecast: mean everage percentage error within ± 5% and 95% confidence intervel of forecast model [131] AI applications for building energy management systems (BEMSs), industrial energy savings, and equipment energy savings -Expert system helped to achieve maximum savings in lighting systems (up to 39.5%) -Building energy savings ranged from 11.39% to 16.22% -HVAC and other equipment had energy savings between 14.07% and 16.66%. [241] AI application in hybrid energy systems (solar, wind, and hydropower) ...
Article
This paper reviews applied single and hybrid solar energy-saving techniques with emphasis on solar chimney, Trombe wall, and photovoltaics for building energy consumption and thermal comfort. Solar energy techniques can be broadly classified into passive and active. Passive strategies refer to collecting, storing, and distributing solar energy without relying on electrical or mechanical components. In contrast, active techniques rely on mechanical and electrical components. The discussed passive devices include solar chimney and Trombe wall. Different design configurations have been reported to influence various buildings' thermal and electrical performances. On the other hand, active solar strategies in buildings including photovoltaic technologies, are discussed. Moreover, applied the hybrid system of these techniques in building is discussed. The review paper critically analyzes these systems' energy and thermal performances to demonstrate their significance in building applications. The significant findings of these studies demonstrate the capability of these systems to provide thermal comfort and considerable energy savings. The review studies indicated that passive architecture could improve thermal (as high as 20%) and visual comfort and reduce electricity consumption by 46%. Besides, several highlights on BIPV and BIPV/T systems are discussed, showcasing their status, recent research, development, and applications toward achieving building energy efficiency. Throughout the review, it is seen that solar technologies applied in buildings have undergone a remarkable revolution, demonstrated significant energy-saving potentials and opening the road for application in net-zero energy buildings. Further research is required on some of the technologies to assess the techno-economic feasibility of these systems in the building sector.
... "Energy savings related to implementing a good monitoring & targeting system can yield significant savings" [5]. For Lee & Cheng [6], typical savings are between 14.07% and 16.66%. This information is obtained through several case studies on monitoring and targeting in industrial and tertiary buildings (commercial, administrative etc.). ...
Article
Earth's average temperature continues to rise, triggering dangerous weather events that occur simultaneously and interact with each other, such as storms, floods, droughts, and wildfires. The main cause of this global warming is the excess of greenhouse gases emitted by human activities. To answer to this climate challenge, most developed countries have taken actions to reduce their carbon footprint. Industry is the third most polluting sector. This article contains research done in a paper industrial site in Belgium to reduce its carbon footprint through energy efficiency. The first part of the paper is a focus on monitoring and targeting system opportunities and barriers. The second part is about motors. Motors are generally among highest consuming equipment in an industrial site. An analysis of motors high efficiency and variable speed drive potential have been done. The savings identified are the following: Financial = $60,336; Environmental = 70.6 CO2 tons (794,000 kWh of electricity, 8.7% of the site electrical consumption). The cost is $128,680 and the payback is 2.3 years.
... Effective Operation and Maintenance (O&M) of Mechanical, Electrical, and Plumbing (MEP) systems may reduce the energy consumption and greenhouse gas emissions of an ageing building. It is estimated that up to 16% of MEP systems' energy consumption during building operations can be saved through proper management [3]. ...
Article
Ageing buildings have become a significant concern for many cities, exacerbated by inadequate management and maintenance of Mechanical, Electrical, and Plumbing (MEP) systems. Building Information Modelling (BIM) enables efficient MEP Operation and Maintenance (O&M) through the digital representation of system information; however, many ageing buildings were constructed without BIM, and manual reconstruction is costly and inefficient due to the sheer number of such structures. Although some studies have proposed methods for automatically recovering BIM from 2D drawings, few are suitable for MEP systems due to the multiscale and irregular shapes of MEP components. To fill this gap, an automatic approach is proposed for recovering MEP model from 2D drawings with three modules: 1) semantic extraction by combining image cropping with Cascade Mask R-CNN to detect and segment multiscale, irregular MEP components; 2) geometric extraction by semantic-assisted image processing to extract contours and skeletons of irregular parts; and 3) Industry Foundation Class (IFC)-based BIM reconstruction via the open-source pythonOCC and IfcOpenShell. The performance was tested on two MEP systems with 335 and 282 multiscale and irregular elements, and the results show that the method recovered BIMs for the two MEP systems in 2.85 s and 0.79 s, with semantic extraction accuracy exceeding 0.9 and geometric error below 5%. This paper contributes to the existing body of knowledge by providing a semantic and geometric-based approach for recovering multiscale and irregular components from 2D drawings. Future studies could further improve the approach by integrating elevation drawings, reconstructing abstract symbols, and aligning text-geometry.
... Global warming and the shortage of energy resources have made energy management system (EMS) a hot topic in the energy sector [1]. Industries have realized that developing improved EMS can potentially improve energy monitoring and budgeting. ...
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Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.
... Global warming and the shortage of energy resources have made energy management system (EMS) a hot topic in the energy sector [1]. Industries have realized that developing improved EMS can potentially improve energy monitoring and budgeting. ...
Article
Full-text available
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic driftadaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.
... It can also enhance transparency and consumer trust, and open up new business models for suppliers, provided that regulatory and technological obstacles are overcome. Furthermore, AI and ML can substantially improve energy security, accuracy in demand, generation, and price forecasting, and consequently, support the implementation of smart grids and the integration of more renewable energy [51,68]. ...
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Digital transformation is a phenomenon introduced by the transformative power of digital technologies, and it has become a key driver for the energy sector, with advancements in technology leading to significant changes in the way energy is produced, transmitted, and consumed. The impact of digital transformation on the energy sector is profound, with benefits such as improved efficiency, cost reduction, and enhanced customer experience. This article provides a review of the impact of digital transformation on the energy sector, highlighting key trends and emerging technologies that are transforming the sector. The article begins by defining the concept of digital transformation, describing its scope, and explaining two conceptual frameworks to provide a deep understanding of the concept. This article then explores the benefits of digital transformation, examines its impact, and identifies its enablers and barriers. Each source examined was analyzed to extract qualitative results and assess its contribution to the researched topic. This paper also acknowledges the challenges posed by digital transformation, including concerns about cybersecurity, data privacy, and workforce displacement. Finally, we discuss the potential developments that are expected in the future of digital transformation in the power sector and conclude that digital transformation has the potential to significantly improve the energy sector’s efficiency, sustainability, and resiliency.
... In addition, the meter sends alerts to homeowners via mobile phone or email when energy consumption approaches 90% of the selected quota, thus improving the dependability and power quality of the intelligent meter. Lee and Cheng (2016) conducted a study that reviewed 305 energy management systems that utilized artificial intelligence for industrial energy savings and equipment energy savings in building energy management systems. According to their findings, the building's lighting systems can contribute up to 39.5% of its energy savings when using an expert system. ...
Article
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New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10–40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5–30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97–42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.
... Department of Energy, 2012). Energy savings achievable using a Building Energy Management System (BEMS) for heating, ventilation, air conditioning (HVAC) and refrigerating equipment, artificial lighting systems, motors and others were analysed in (Lee & Cheng, 2016). Energy savings from BEMS increased from 11.39 to 16.22% yearly from 1976 to 2014. ...
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This paper analyses challenges and opportunities for improving energy efficiency in small and medium enterprises (SMEs) by reviewing research design and results out of seven Euro-pean projects: SPEEDIER, SMEmPower Efficiency, E2Driver, Innoveas, Triple-A, DEESME and ICCEE. These projects aim to improve SMEs' awareness of energy efficiency and support an effective decision-making-oriented approach to it. Drivers and barriers to energy efficiency improvements in European SMEs of various industrial sectors have been investigated by means of surveys, focused group discussions and interviews. A meta-analysis of the results of the seven EU projects was carried out to discover trends related to energy efficiency in European SMEs; this was supported by the use of a unifying analytic framework that enabled merging and cross-validation of the findings of the seven projects. The analysis indicated, by means of new data collected by the seven projects , that staff training, facilitation of energy audits, development of corporate policy measures and collaboration between SMEs involved in the same supply chain are key mechanisms to improve the uptake of energy efficiency measures in SMEs which has significant potential to achieve higher energy savings and energy cost reductions.
... Equations Single/multi-objective [51] (50) Single-objective [53] (44) Single-objective [62] (22) Single-objective [63] (7) Single-objective [77] (3) and (43) Multi-objective [79] (13) Single-objective [87] (52) Single-objective [91] (1), (29), (35), (40), (41), and (48) Multi-objective [92] (2) and (42) Multi-objective [93] (4)and (46) Multi-objective [94] (5) Single-objective [95] (6) Single-objective [96] (8) Single-objective [97] (9) Single-objective [98] (10), (11), (27), and (28) Multi-objective [99] (12) Single-objective [100] (14) Single-objective [101] (15) Single-objective [102] (16) and (18) Multi-objective [103] (17), (31), and (45) Multi-objective [104] (19) Single-objective [105] (20) Single-objective [106] (21), (32), and (33) Multi-objective [107] (23) and (47) Multi-objective [108] (25) and (51) Multi-objective [109] (24) Single-objective This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. ...
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Chapter
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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.
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A power interchange operation, in which electric power generated by multiple residential solid oxide fuel cell cogeneration systems (R-FCGSs) is shared among residences in a housing complex with no reverse power flow to a commercial electric power system, has a high energy-saving effect as compared with a stand-alone operation of individual systems. To further improve its energy-saving effect, the optimal sizing of the R-FCGS for this power interchange operation is discussed from the energy-saving viewpoint by conducting optimal operational planning based on mixed-integer linear programming. The energy-saving effect of the power interchange operation is analyzed for 14 different scales of the R-FCGSs with the rated electric power output ranging from 0.2 to 1.5 kW per 0.1 kW. The analysis for a housing complex with 20 residences located in Japan reveals that the power interchange operation has an advantage over the stand-alone operation in terms of energy saving for any scale of the R-FCGS investigated in this study. Furthermore, it is found that the 0.6-kW R-FCGS is the optimal scale for the power interchange operation in terms of the energy saving and saves the annual primary energy consumption of 17% as compared with the conventional energy supply with no R-FCGS.