Building Energy Management System Research Articles

Occupancy-based Energy Management System in Campus Building: A Case Study in Electrical Engineering Department Building, ITN Malang

Building energy management systems provide an effective method for energy saving. An occupancy-based energy management system considers the occupants' behavior that affects energy consumption. This paper presents the investigation of different scenarios based on the occupancy to control the energy consumption in the Electrical Engineering Department Building, ITN Malang. The results show that switching off the appliances when the occupants go home in the afternoon reduces the monthly energy consumption by 33.95%. The result indicates that the occupants' awareness regarding energy saving has recently been low. Thus, the occupancy sensor and automatic time scheduling for the appliance control are suggested to be installed.

Assessing the performance of smart buildings and smart retrofit interventions through key performance indicators: Defining minimum performance thresholds

Smart technologies play a vital role in facilitating the response of buildings to the external conditions, including climate, grid, and the internal building requirements such as user needs. A salient concern lingers in relation to existing buildings due to their increasing energy consumption. Around 35 % of EU buildings are older than 50 years and 90 % are built before the nineties. In this sense, smart retrofitting represents a key step towards achieving energy-responsive flexible buildings. Quantifying building energy performance with appropriate Key Performance Indicators is a critical step towards achieving decarbonization goals in both existing and new buildings. In this paper a group of five representative indicators has been selected to measure the energy performance of smart features in retrofitted and new buildings, also identifying distinct performance thresholds. Therefore, each threshold defines minimum acceptable and top performing values for the indicators. Accordingly, thresholds are set first based on previous literature and performance data, then a Logical Evaluation Methodology is used to identify suitable range of thresholds. Results of this paper propose a quantified definition for smart retrofitting, which involves transforming an existing building into a Smart Building. A smart building is a nearly Zero Energy Building that achieves primary energy savings of 30 % to 80 % and can adapt to changing climate and grid conditions. It should communicate with users and predict operational failures using a Building Energy Management System. Additionally, it should enable load shifting in response to renewable energy source production and electricity prices by 30 % to 70 % annually, while minimizing grid interaction to 10 % to 30 % on an hourly basis throughout the year. Furthermore, it should allow for RES self-consumption of 30 % to 70 % and cover 20 % to 70 % of the load with RES annually. Finally, the indicators are tested on a case study in Italy within a Horizon 2020 project to validate the thresholds.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

A Study on the Energy Efficiency of an Energy Management System for Convenience Stores

This research presents a solution for improving energy efficiency in convenience stores by implementing a building energy management system (BEMS) that uses new logic control in air conditioning and refrigeration systems. These systems currently consume the most energy in convenience stores. Implementing this system not only reduces the energy consumption of the compressors in both systems but also minimizes energy loss due to low desired temperatures in the sale area while maintaining the cabinet temperature at the same level. An experiment was conducted at a 314-square-meter convenience store that was open from 6:00 a.m. to 11:00 p.m., and we demonstrated a 4.4-year payback period by controlling AC units close to the desired sale-area temperature of 25 degrees Celsius and increasing the suction pressure at a medium-temperature CDU by 0.3 bar or 31 kPa. This resulted in energy savings of 7.1 kilowatt-hours per day, or 2591.5 kilowatt-hours per year, for the air conditioning system and 2.8 kilowatt-hours per day, or 1022.0 kilowatt-hours per year, for the refrigeration system, resulting in a total energy savings of 9.9 kilowatt-hours per day, or 3613.5 kilowatt-hours per year. The convenience store can use the results of this research to improve the energy efficiency of its cooling system, which includes air conditioning and refrigeration systems, thereby promoting sustainable energy conservation.

A Systematic Literature Review on Energy Efficiency Analysis of Building Energy Management

Government agencies, energy consumers, and other societal groups have all shown concern and attention for the energy management of buildings. Relevant statistical data, however, indicate that most public buildings continue to consume large amounts of energy overall and that the issues of low energy usage and energy waste have not materially improved. As a result, this study reviewed the state of progress and potential directions for future research in the field of building energy management in public buildings using a data-driven approach. Relevant studies were obtained from three databases—Web of Science, Scopus, and China National Knowledge Infrastructure—based on certain search phrases. The text mining program VOS viewer was then used to examine the material. We provide a thorough examination of the study techniques and material, as well as a visual representation of the keywords and current state of the field. According to this study, the range of data processing outcomes; the flexibility of research system standards; and the availability of a comprehensive, unified assessment system are the main factors contributing to the practical issues facing building energy management today. Based on the geographic distribution and state of energy development, this study is the first to examine possible research avenues for building energy management in public buildings through cross-fusion research on passive energy-saving design and subjective behavioral energy-saving. It offers a foundation for developing the building energy management system best practice model in the future.

Optimizing the thermal performance of phase change materials in building applications using deep reinforcement learning and Bayesian optimization

This research presents a novel methodology for Deep Reinforcement Learning (DRL) and Bayesian Optimisation of the thermal performance of PCMs in building operations. The developed models utilise a unique and large dataset comprising 1500 building thermal profiles, simulated for various climates and building setups obtained from our industry partners and as open data. PCM-based systems are deployed for thermal insulation of building envelopes to regulate indoor temperature conditions and reduce the need for heating and cooling systems, resulting in enhanced energy efficiency. Through the real-time management of the thermal efficacy of PCMs using the DRL method trained on the large dataset and fine-tuning of the underlying model parameters using Bayesian Optimisation, the optimised system achieves energy saving in heating and cooling load of up to 45 percent, along with the induced reduction in CO2 emission. At the same time, DRL contributes to decreasing the thermal fluctuation in the indoor temperature and keeps it in the narrow range of 1.2 °C in case of high thermal variability scenarios. Currently, the best performance is reported in the literature. This research exemplifies the potential of DRL and Bayesian optimisation in sustainable building. It depicts the applications of advanced intelligent computing algorithms with big building energy data as a novel, robust and superior approach for optimising real-world building energy management systems. The methodology and the improvements in energy savings in thermal and energy management of buildings highlight the novelty and potential benefit of the implemented research as a new intellectual property towards sustainable building design.

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).

Analyzing the implementation of predictive control systems and application of stored data in non-residential buildings

In non-residential buildings, building energy management systems (BEMS) and the application of data hold significant promise in reducing energy consumption. Nevertheless, BEMS have different levels of complexity, benefit, and limitation. Despite the advanced technologies and improvements in building operation, there is a clear gap in the actual performance of buildings that has been attributed to the adoption of advanced technologies. Consequently, there is an increasing need for researchers and practitioners to study current practices in order to identify and address the challenges that compromise the core objectives of BEMS. For this reason, this paper aims to validate three research questions: (i) to examine the current state of BEMS and its functionalities; (ii) to analyze the type of control used; (iii) and to determine the availability of historical data compiled by BEMS and its application in non-residential buildings. A survey of 676 buildings and interviews with building professionals were conducted. The findings confirmed that most of the buildings applied BEMS with scheduled control. In addition, a lack of digitized data for analysis and predictions was detected. Indeed, only 0.60% of the investigated buildings implemented predictive control. Finally, using hierarchical clustering analysis, responses were grouped to analyze similarities between them. The study findings help to develop targeted actions for implementing predictive control in non-residential buildings.

Interpretable feature selection and deep learning for short-term probabilistic PV power forecasting in buildings using local monitoring data

Accurate probabilistic forecasting of photovoltaic (PV) power is crucial for optimizing energy scheduling in smart buildings and ensuring the low-carbon, efficient operation of building energy management systems (BEMS). However, existing feature selection techniques fail to guarantee that the selected features genuinely impact the output of forecasting models. Additionally, traditional black-box deep learning models lack clarity on whether their output truly relies on those selected features. These challenges limit the accuracy of forecasting models. To address these challenges, a novel methodology named temporal importance model explanation and temporal fusion transformers (TIME-TFT) model is proposed. Firstly, the TIME method is employed for feature selection, and interpretable outputs are used to identify important global features. Secondly, the TFT model is then utilized for forecasting tasks, providing interpretable outputs to track back to the features that TFT model pays attention to. Finally, the consistency between the interpretable outputs of TIME method and TFT model is examined to confirm predictions are based on genuinely learned selected features. Empirical studies demonstrate the superiority of the proposed TIME-TFT system, outperforming comparable models with an R2 of 0.9546. In summary, the interpretable outputs not only improve the accuracy of predictions but also provides visual evidence for predictions, thereby bolstering effectiveness and credibility in engineering practices.

Barriers to the adoption of energy management systems in residential buildings

PurposeThis study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.Design/methodology/approachThis study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.FindingsThe findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.Research limitations/implicationsThe specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.Practical implicationsPeople’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.Originality/valueThis research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.

Resilient energy management strategy in smart residential buildings considering price attack: An aggregative game perspective

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

Home Energy Management System for a Residential Building in Arctic Climate of Norway Using Non-Intrusive Load Monitoring and Deep Learning

Home Energy Management System for a Residential Building in Arctic Climate of Norway Using Non-Intrusive Load Monitoring and Deep Learning

Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT

Enhancing Energy Efficiency With Smart Building Energy Management System Using Machine Learning and IOT

Opposing approaches for enhancing energy efficiency in buildings

Abstract This paper proposes two opposing approaches for enhancing energy efficiency in buildings as a part of combating climate change and summarizes interim outcomes from two ongoing research projects. The first project focuses on adopting sustainable design methodologies in new buildings, and the second one aims at ’greening’ old but otherwise economically viable and usable buildings. Finally, the paper points to the potential for an integrated platform for efficient energy management system for buildings for clients and/or owners, albeit with addressing a range of issues.

Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision

Active building energy management can facilitate the development of low-carbon buildings and support flexible operations of future smart cities, thanks to advancements in digitalization To fully leverage these benefits, it is essential to integrate diverse objectives and engage multiple stakeholders. However, a gap remains in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware prosumer with flexible energy consumption. An existing building energy management system was enhanced by integrating a model predictive control strategy. The setup reduced equivalent carbon emissions from electricity imports and provided flexibility to the energy system. The experimental results indicate an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggest that flexibility was provided without the risk of rebound effects, as flexibility was quantified and communicated to the system operator in advance. This study demonstrates the feasibility of low-carbon buildings and their support for flexible energy systems, while also identifying and discussing practical scalability challenges.

Application of Bayesian models in building information modeling of residential buildings

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.

From bricks to bytes: Verifiable data for decarbonizing the building sector

Amidst the pressing need to combat climate change and curb greenhouse gas (GHG) emissions, the building sector emerges as a pivotal sector, substantially impacting worldwide emissions. Despite efforts to improve energy efficiency and incorporate non-fossil energy sources, the sector still lags in achieving the necessary decarbonization goals. Existing Building Energy Management Systems primarily prioritize economic criteria, overlooking the vital aspect of emissions reduction. Energy Informatics and Information Systems hold the potential to bridge this gap by enabling precise and verifiable GHG emissions accounting, end-to-end real-time tracking, and automated verification within Energy Management Systems (EMS). This paper presents research on designing the advancement of EMSs in the form of a Building Energy Emission Management System (BEEMS) leveraging verifiable emission data for emission-based actions. The central research question revolves around designing BEEMS to facilitate emission-based actions based on verifiable data. Following a multi-step approach, the research methodology encompasses a comprehensive literature review and iterative evaluation of our design principles through a workshop and semi-structured interviews with experts from industry and research. The contributions include a conceptual architecture of a BEEMS and six design principles for future BEEMS development. Ultimately, this research strives to facilitate end-to-end verifiable GHG emissions management in the building sector to enable emission-based energy consumption decisions, contributing to the existing body of knowledge of the Energy Informatics field on BEEMS.