Figure 2 - uploaded by Giacomo Verticale
Content may be subject to copyright.
Source publication
The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling...
Context in source publication
Context 1
... EMS is able to take educated decisions based on the available information collected during the lifetime of the system and stored in the so-called data repository, depicted in Figure 2. In order not to overload the repository with too many data which are not of interest except for the data provider (which would see the repository as a local database) or for the single actor (that would use the repository as a data exchange mean), only information exploited by more than two (sub)systems is stored and shared. ...
Similar publications
Integrating fleets of electric vehicles (EVs) into industrial applications with smart grids is an emerging field of important research. It is necessary to get a comprehensive overview of current approaches and proposed solutions regarding EVs with vehicle-to-grid and smart charging. In this paper, various approaches to battery modeling and demand r...
With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response...
The highly varying character of district heating (DH) demand results in low capacity utilization of the DH plants, as well as increased use of fossil fuels during peak demand. The aim of this study is to present an overview and a comprehensive classification of measures intended to manage these load variations. A systematic literature review was co...
The introduction of explicit demand response (DR) in the electricity markets for energy, capacity and ancillary services requires a definition of the customer baseline load (CBL). Such counterfactual-that is, what the customer would consume in the absence of demand response-is necessary to measure the effective performance of a demand resource and...
Citations
... Campus buildings consume large energy, and the majority of campus buildings are equipped with building control systems . Building control together with an energy management system (EMS) can increase the energy efficiency of campus buildings and the potential of providing energy flexibility to the grid due to larger automation in the energy control (Barbato et al. 2016;). There are three types of occupants in the campus buildings, researchers/teachers, students and administration (including management). ...
Due to large energy consumption and equipped building control systems, the majority of campus buildings have the potential to increase energy efficiency and provide energy flexibility. Among the three main types of occupants in the campus buildings (researchers/teachers, students, administration/management), students spend most of their time on learning activities in the campus buildings, and the energy performance can influence their learning performance. Therefore, this paper conducts a questionnaire targeting student occupants at a large engineering faculty in a Danish university to investigate occupants’ experience of energy control, indoor comfort, and options of energy flexibility in campus buildings. In total, 267 fully completed and usable questionnaires were received. The dataset is available in.xlsx format, and the questionnaire that was used to collect the data is also provided together.
... The fourth cluster (the yellow one) consists of studies discussing smart energy and transport. The use of new technologies for energy management and transportation attracted many scholars' attention (Al-Smadi et al. 2015;Barbato et al. 2016;Choi 2019;Fujimoto et al. 2020). The smart city is a sustainable solution in all sub-domains of energy-related processes and infrastructure, including energy generation and storage, infrastructure, facilities, and transport systems. ...
Over the last three decades, many studies have been conducted on smart cities. Studies range from enhancing the quality of life for citizens to boosting the local economy, improving transportation and traffic management, increasing environmental awareness, and improving citizen and business interactions with the government. Due to the importance of smart cities to various stakeholders and the associated benefits and challenges of implementation, the smart city concept has garnered significant attention from researchers across various disciplines. The growing number of publications necessitates a systematic and comprehensive examination of smart city research. This paper aims to sketch out a research landscape, as a systematic understanding of smart cities is still lacking. A bibliometric analysis of 4760 papers collected from the Web of Science (WOS) is used to determine the publication year, country, source, institution, and keyword co-occurrence. The subfields of smart city research are identified and evaluated based on co-citation analysis and the application of the research potential evaluation (RPE) model. Additionally, the study employs the main path analysis (MPA) method to visually analyze the literature’s development trend and citation network and examine the knowledge diffusion associated with smart city research. The keyword clustering and MPA results indicate that smart city research is primarily focused on sustainability and governance, new technologies (e.g., the internet of things, blockchain, new computing paradigms and artificial intelligence), smart energy, transportation, and interactive applications. Six subfields of smart city research are identified, including Industry 4.0 technologies, digital twin technologies, smart tourism, smart city governance, smart city conceptualization and implementation, and sustainability. Industry 4.0 technologies and sustainability represent the two key subfields with the highest number of publications among these subfields. Based on the results of the RPE model, the theme of Industry 4.0 technologies has the strongest development potential and has stayed a popular topic over the past few years. Through MPA, we also observed a recent trend towards examining how artificial intelligence and open data platforms contribute to smart cities. By combining bibliometrics and MPA, we better understood the current state of smart city research and identified future directions for the field.
... The objective of the MPC is to maintain indoor thermal comfort at the lowest possible operational cost. The comfort assessment is based on zone air temperature since it is the main factor influencing the thermal comfort perceived by the occupants in a building [36]. Note that zone air temperature is used in this work instead of zone operative temperature because it is the only measured temperature in the zone. ...
Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty.
... Note that there exist other environmental factors that should be addressed when providing comfort for occupants, like thermal radiation, humidity, and air speed, or personal factors like activity and clothing. For the sake of clarity, our comfort assessment is based on temperature only since it is the main factor influencing the thermal comfort perceived by the occupants in a building [76]. ...
Buildings need advanced control for the efficient and climate-neutral use of their energy systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two powerful control techniques that have been extensively investigated in the literature for their application to building energy management. These methods show complementary qualities in terms of constraint satisfaction, computational demand, adaptability, and intelligibility, but usually a choice is made between both approaches. This paper compares both control approaches and proposes a novel algorithm called reinforced predictive control (RL-MPC) that merges their relative merits. First, the complementarity between RL and MPC is emphasized on a conceptual level by commenting on the main aspects of each method. Second, the RL-MPC algorithm is described that effectively combines features from each approach, namely state estimation, dynamic optimization, and learning. Finally, MPC, RL, and RL-MPC are implemented and evaluated in BOPTEST, a standardized simulation framework for the assessment of advanced control algorithms in buildings. The results indicate that pure RL cannot provide constraint satisfaction when using a control formulation equivalent to MPC and the same controller model for learning. The new RL-MPC algorithm can meet constraints and provide similar performance to MPC while enabling continuous learning and the possibility to deal with uncertain environments.
... Typical heating period J a n 1 7 J a n 1 9 J a n 2 1 J a n comfort for occupants, like thermal radiation, humidity, and air speed, or personal factors like activity and clothing. For the sake of clarity, our comfort assessment is based on operative temperature only since it is the main factor influencing the thermal comfort perceived by the occupants in a building [35]. ...
According to the International Energy Agency (IEA), energy use accounted 9384 Mtoe in 2015. The building sector is the largest energy-demanding sector, with over one-third of final energy use globally. In addition, Building Energy Management (BEM) allows to unlock flexibility for the grid(s) by Demand Response (DR) strategies. This facilitates the integration of more Renewable Energy Sources (RES), thus achieving lower overall emissions of CO2 in the global energy system. However, most of the existing Building Management Systems (BMS) are based on Rule Based Controllers (RBC). Advanced optimal control techniques like Model Predictive Control (MPC) or Approximate Dynamic Programming (ADP) have proven to outperform RBC in both simulation and real implementation.
This PhD research seeks to develop novel algorithms to increase the share of RES and harness the available flexibility aiming for the broad use of optimal control in buildings. To achieve this main objective, the developed algorithms are innovative and effective in the optimal control sense and, at the same time, prove that they can be applied in reality with minimal amount of engineering effort.
In a first step, a common terminology is unambiguously formulated in a set of definitions. As such inputs and outputs are clearly defined which enhances the reproducibility of results. At the same time the requirements for a benchmark setup to assess optimal control of energy systems in buildings are being formalized. Then, the different ‘modelling’ approaches are compared with respect to control performance for MPC and ADP separately. This comparative study leads to insights in when one method is preferred over the other which allows developing a decision tree to select the most appropriate approach.
An innovative hybrid approach is developed to enrich the data-based methods by models in order to learn better and/or faster, which results in a more performant regression. Moreover, variations within the MPC/ADP formulations are investigated. The impact of these variations on the generic character, scalability, engineering cost and computation cost is investigated. Finally, the algorithms developed in this PhD are validated by simulations as well as against measurement data.
... V. . A smart campus is in charge of energy consumption scheduling, while its telecommunications infrastructure serves as the place where data transfers are conducted (Barbato et al., 2016). Integrating cutting-edge technology, a smart campus captures real-time data on energy usage, renewable energy power generation , air quality, and more (Alrashed, 2020). ...
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. In addition to its theoretical contribution to scientific research on sustainable artificial intelligence in energy management, the research utilizes a novel topic modeling method in exploring scientific texts and identifying challenges and possible solutions. A variety of solutions was incorporated, including huggingface tool or elbow method, to address these challenges.
... The two themes represent two elements of education: one dealing with the learning contents, and the other with behavioral outcomes of developing smart campuses.To build a model of smart campuses, we should focus on incorporating IoT into the infrastructure, with subsequent implementations of smart apps and services, with smart educational tools and pedagogies and smart analysis as well [97]. A smart campus is in charge of energy consumption scheduling, while its telecommunications infrastructure serves as the place where data transfers are conducted [110]. Integrating cutting-edge technology, a smart campus captures real-time data on energy usage, renewable energy power generation , air quality, and more [111]. ...
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.
... In the field of artificial intelligence monitoring system construction and processing, the convolution of the signal is multiplied by the corresponding frequency domain [5,6]. Assuming that the clear image is f(i, j) and the noise function is n(i, j), the fuzzy image function g(i, j) obtained is the corresponding relationship between the three as follows: ...
With regard to the development of colleges and universities, ensuring the quality of education is the fundamental goal and main task of teaching daily management. With the continuous improvement of the application level of the Internet and other information technologies, the construction of smart campus in colleges and universities in China is rapidly advancing. This paper studies the construction and innovation strategy of the public sports quality monitoring system and discusses the changes in college students’ sports quality after the introduction of smart campuses from the perspective of artificial intelligence and the creation of smart universities. In this paper, the field survey method and other research methods are combined to study, and in the process of data storage, SQL Server database platform is used to store the data. This study shows that the proportion of each element of physical state management has changed significantly before and after college entrance. According to the data, since the introduction of smart campus real name system identification, tracking data, and evaluation functions, the number of college students’ physical exercise has increased significantly. The number of students with exercise plan in school 1 has increased from 70 to 222, and that of school 2 has increased from 49 to 199. Before the introduction, the students were very satisfied with the learning effect of physical education, which was 40.12% and increased to 45.70% after the introduction. Before the introduction, the students were very satisfied with the sports equipment, which was 30.12% before the introduction and increased to 35.24% after the introduction. Therefore, building a system for monitoring the quality of public sports in universities is very important for improving the quality of education in public sports in universities and plays an active role in promoting the physical and mental health of students.
... This is evident in the Smart Campus project reportage in extant literature. Examples of such projects include the development of an anytime-anywhere learning within a Smart Campus environment [46], Smart parking [11,47,48], frameworks for modeling movements on a Smart Campus [49], development of platforms for energy management and optimization on campuses [50][51][52], dynamic timetabling systems [53], the use of apps for location directions and information dissemination purposes [54], real-time space utilization measurement [55], development of a context-aware Smart classroom [56][57][58][59], and the use of digital platforms for IoT-based disaster management [60]. ...
An identification of strengths, weakness, opportunities, and threats (SWOT) factors remains imperative for enabling a successful Smart Campus transition. The absence of a structured approach for analyzing the relationships between these SWOT factors and the influence thereof on Smart Campus transitions negate effective implementation. This study leverages a systems thinking approach to bridge this gap. Data were collected through a stakeholder workshop within a University of Technology case study and analyzed using qualitative content analysis (QCA). This resulted in the establishment of SWOT factors affecting Smart Campus transitions. Systems thinking was utilized to analyze the relationships between these SWOT factors resulting in a causal loop diagram (CLD) highlighting extant interrelationships. A panel of experts drawn from the United Kingdom, New Zealand, and South Africa validated the relationships between the SWOT factors as elucidated in the CLD. Subsequently, a Smart Campus transition framework predicated on the CLD archetypes was developed. The framework provided a holistic approach to understanding the interrelationships between various SWOT factors influencing Smart Campus transitions. This framework remains a valuable tool for facilitating optimal strategic planning and management approaches by policy makers, academics, and implementers within the global Higher Education Institution (HEI) landscape for managing successful Smart Campus transition at the South African University of Technology (SAUoT) and beyond.
... In reality, the market trends in each commercial building reflect the occupancy rate and trend of variation of the building's citizens. For an MG having different typologies of buildings according to their specific, the optimal coordination of DR among the buildings with complementary load patterns can minimize the passive effects of load shedding on the end users [14,15]. ...
This chapter introduces the distributed demand-side management strategies for modern power system with microgrids (MGs). The first distributed demand response (DR) application is direct load control. It has been an open issue to address the peak load shedding in a network of multiple types of buildings, like a campus MG, where each building is concerned about maximizing its own end user's satisfaction level. A decentralized DR model is developed to realize peak load shaving under the incentive of minimizing the affected population caused by the load interruption. It is solved by an alternating direction method of multipliers–-based DR algorithm considering complementary building consumption patterns. A simulated system based on the consumption data of the University of Connecticut cogeneration plant is built to validate the effectiveness of the proposed DR control. The second distributed DR application is emergency DR, which is critical for increasing power system resilience in disaster recovery. This chapter presents a game-theoretic DR approach for MG-aided restoration service. The transmission line sensitivity analysis is applied to quantify the physical impacts of energy trading on the system stability level. Taking these quantified impacts as the stability incentive rewards earned by MGs aside from the economic incentive rewards, the interactions between the utility and MGs become highly combinatorial. A two-level game structure is built to model energy trading between the utility and MGs for the restoration service. The first level is a Nash game to formulate the noncooperative relationship among MGs. The second level is a Stackelberg game led by the utility for determining the electricity price, which could maximize restoration service cost efficiency. Case studies on IEEE 6-bus and 57-bus test systems demonstrated the effectiveness of the proposed distributed DR approach on energy trading decision support for MG-aided restoration service.
