Deep learning and multi-objective optimization for real-time occupancy-based energy control in smart buildings.

This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.

For an effective energy management in intelligent buildings it is necessary to gather information about position/absence of people and the level of population. In this paper the smart occupancy detection system using the active RFID is developed to satisfy such a demand based on the results of previous research. First of all the design considerations and functions of the system are introduced. In sequence the functions of the system is presented, and then the performance of the developed system is tested and verified through various field tests. The developed core technology can be also applied to other fields such as security, healthcare, smart home, etc.

Intelligent energy management in off-grid smart buildings with energy interaction

Intelligent context-awareness system for energy efficiency in smart building based on ontology

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

With the swift growth of hydrogen production and storage technology, the progress of hydrogen energy storage systems (HESSs) will bring radical revolution to the composition of energy and power system. The combination of HESSs and battery energy storage systems (BESSs) for coordinated optimization can solve the imbalance between supply and demand of various energy sources, while it can improve energy efficiency. In order to ensure the effectiveness of BESSs and HESSs planning, aiming at the minimum life cycle cost (LCC), system network loss, tie line exchange power deviation, load fluctuation, and voltage fluctuation, this paper utilizes multi-objective particle swarm optimization (MOPSO) to solve the Pareto non-dominated solution set of ESSSs location and capacity planning scheme. Besides, the grey target decision based on the entropy weight method (EWM) is used to select the best compromise solution in the Pareto non-dominated solution set. In addition, the typical operation scenario set of source load is obtained by fuzzy kernel C-means (FKCM) clustering algorithm, while the simulation analysis is carried out on the basis of the extended IEEE-33 bus system. The simulation results show that MOPSO realizes the minimum LCC of the electric-hydrogen hybrid energy storage system, upon which its voltage quality, power stability, network loss, and load fluctuation are better than those non-optimized.

Objective: This study aims to identify key characteristics of smart buildings that enhance sustainability and energy efficiency in educational institutions. By leveraging artificial intelligence, particularly machine learning, the study seeks to optimize energy consumption and improve learning environments. Theoretical Framework: Smart buildings integrate digital and communication technologies to enhance educational infrastructure. Despite their benefits, adoption in developing countries remains limited. This study builds on theories of sustainable energy management and computational optimization, emphasizing machine learning’s role in predictive energy modeling. Method: A machine learning model was developed to predict energy consumption in educational institutions. The k-nearest neighbor (k-NN) algorithm was applied using open-access data from the University of Durrës Building Energy Management System. Model validation was conducted through comparative analysis, assessing prediction accuracy and energy-saving potential. Results and Discussion: The model achieved an average relative error of 18.26%, confirming its predictive capability. Smart features enabled energy savings between 44.8% and 58.3%, depending on analyzed spaces. Additionally, an AI-powered interactive dashboard was proposed for real-time energy monitoring and optimization, aiding facility managers. Research Implications: This study underscores the role of smart buildings in promoting sustainability and reducing operational costs. AI-driven energy management systems can significantly enhance institutional sustainability practices. Originality/Value: This research demonstrates the effectiveness of machine learning in energy consumption prediction and proposes an AI-based solution for sustainable energy management in educational buildings. The findings provide valuable insights into integrating smart technologies for sustainability and efficiency.

A two-stage multi-objective optimization method for envelope and energy generation systems of primary and secondary school teaching buildings in China

This article is important for professionals related to the world of infrastructure and building, because it provides what is necessary to understand energy management in buildings.It is also important for the technological impact it provides to users [22].This research analyzes the energy management in smart buildings in Peru through methodologies such as the review of articles published in the Scopus database using the VOSviewer software as a data analysis tool.This analysis has a period from the XXI century to the year 2021.Graphs related to the research of the database were obtained where the words "intelligent building" and "energy management" are highlighted with greater congruence and mentioned in these journals.Figures were obtained worldwide and in Peru.The results allowed us to understand that smart buildings are becoming more important in developed countries and opening new markets since there are unmet needs of users.In the analysis of Peru, the opposite happens since there are minimal scientific contributions related to the subject.They still do not have intelligent buildings 100% but there are some buildings where energy management is carried out in minimum proportion.An energy management software related to big data called smart building was also found, which has the objective of managing energy consumption in each building to reduce the carbon footprint emitted by buildings.This research is intended to guide you to start with in-depth research and implementation projects for engineers and architects interested in developing and managing intelligent edifications.Nowadays, the importance of studying this topic is to take care of the environment as social and professional responsibility.For this reason, the professionals should opt for better strategies for energy management thus positively impact Peru.

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits reported in real-world deployments. Building-scale systems typically integrate 20–150 kW PV and achieve ~10–18% energy-efficiency gains enabled by DC distribution. Industrial-park deployments scale to 500 kW–5 MW, with renewable self-consumption often exceeding 50% and CO2 emissions reductions of ~40–50%. Community-level setups commonly report 10–15% efficiency gains and annual CO2 reductions on the order of tens to hundreds of tons. Key barriers to large-scale adoption are also discussed, including multi-energy coordination complexity, high upfront costs and uncertain business models, limited user engagement, and gaps in interoperability standards and supportive policies. Finally, we outline research and deployment priorities toward open and interoperable PEDF architectures that support cross-sector integration and accelerate the transition toward carbon-neutral (and potentially carbon-negative) built environments.

The common approach to model occupants behaviors in buildings is deterministic and consists of assumptions based on predefined fixed schedules or rules. In contrast with the deterministic models, stochastic and agent based (AB) models are the most powerful and suitable methods for modeling complex systems as the human behavior. In this paper, a co-simulation architecture is proposed with the aim of modeling the occupant behavior in buildings by a stochastic-AB approach and implementing an intelligent Building Energy Management System (BEMS). In particular, optimized control logics are designed for smart passive cooling by controlling natural ventilation and solar shading systems to guarantee the thermal comfort conditions and maintain energy performance. Moreover, the effects of occupant actions on indoor thermal comfort are also taken into account. This study shows how the integration of automation systems and passive techniques increases the potentialities of passive cooling in buildings, integrating or replacing the conventional efficiency strategies.

This article presents the electrostatic actuation performance of micromirror arrays for intelligent active daylight control and energy management in green buildings using a capacitive–voltage (C-V) measurement technique. In order to understand how geometric hinge parameters, initial opening angles, and materials affect the overall efficiency and functionality of the system, micromirror arrays have been analyzed using C-V measurements considering (i) full and broken hinge structures, (ii) 90° and 130° initial tilt angles (Φ), and (iii) different material layer combinations. The measurement results indicate that both an increase in the Young’s modulus of the applied materials and increasing the initial tilt angles increase the threshold voltages during the closing process of the micromirrors.

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications.

Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost

Experimental assessment of the interaction between indoor air quality and thermal comfort in naturally ventilated secondary classrooms in southern Spain