Building Energy Management System Research Articles

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 indicated 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 suggested 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.

Optimizing the characteristic cooling curves of PCM-integrated thermally active buildings: Experimental and numerical investigations

Phase change materials (PCMs) exploit their cyclic phase change (solidification and melting) to enable latent heat storage. While incorporating them in thermally activated building systems can potentially enhance energy savings and thermal comfort, they can introduce significant challenges to the building energy management system. This work first reports the result of small-scale side-by-side experiments on two thermally active cuboids with and without BioPCMs at different supply water and PCM melting temperatures, as well as irradiance levels. Next, a dynamic model is developed in TRNSYS v18.05 for a typical design of an office space under hot desert climate conditions. The model is then integrated into a multi-objective optimization routine using NSGA-II algorithms to identify the optimal characteristic cooling curves, melting temperatures, and PCM thicknesses that simultaneously minimize energy consumption and maximize the percentage of comfortable hours. The experimental observations of the PCM-integrated cuboid highlighted energy savings between 80.45 and 84.56 %, compared to the reference cuboid, at nearly the same levels of indoor temperatures. The PCM panels reduced the cooling capacity of the radiant floor but also dampened air temperature variations by up to 83.17 %. Besides, higher solar irradiance levels, encountered in desert areas, were found to enhance the cooling capacity by up to 46.8 %. The Pareto solutions highlighted the possibility of achieving acceptable unmet hours of 3.16 % using PCMs and without the need for an assisting air system, compared to 14.1 % without PCMs. Besides, at the same unmet hours of the reference system, the PCMs reduced the total energy consumption and the peak cooling power by 9.75 and 41.2 %, which translates into substantial operation and capital investments, respectively. Moreover, the study demonstrated by example the importance of calibrating the commonly used cooling curves of thermally active buildings when planning for PCM integration.

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Innovative hybrid prediction method integrating wavelet threshold decomposition and entropy-based model selection strategy for building energy consumption prediction

Building energy prediction plays a pivotal role in the building energy management system, and an efficient prediction method is crucial to ensure the effective operation of building systems. Existing research enhances prediction accuracy by combining decomposition algorithms with prediction methods to separate non-stationary information from the original sequence. However, the decomposition process and the subsequent prediction of multiple decomposed subsequences result in significant time overhead, making it unsuitable for scenarios with high temporal requirements, such as short-term energy consumption forecasting. Additionally, a single prediction model struggles to adapt to the diverse features of decomposed subsequences, creating prediction accuracy and efficiency bottlenecks. Addressing these challenges, this study proposes a hybrid prediction method that combines wavelet threshold decomposition (WTD) with an entropy-based model selection strategy. The method initially utilizes the efficient WTD algorithm to decompose the original energy consumption data into three fixed subsequences. Subsequently, the permutation entropy for each subsequence is computed, and based on the comprehensive performance of four prediction models (ANN, LSTM, CNN-LSTM, and TCN) on sequences with different entropy values, the optimal prediction model is selected. Finally, the prediction results of the subsequences are aggregated to obtain the ultimate prediction outcome. Experimental results demonstrate that the proposed method significantly outperforms baseline methods in both prediction speed and accuracy across five real building energy consumption datasets with distinct operational modes.

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Demand side management optimization and energy labeling of multi-purpose buildings

A multi-purpose building with different applications such as residential, office, cultural, restaurant, parking, etc. is analyzed in this research. The facilities in many multi-purpose buildings in developing countries are considered having old energy systems with high energy consumption. Application for national energy labeling require optimizing the energy consumption. However, many such buildings in Iran are not awarded the above-mentioned label. The present study aims to assess a multi-purpose building which is not able to get standard energy label with existing situation. To reduce energy consumption in such buildings, solutions such as applying phase-change materials (PCM) in exterior walls, installation of photovoltaics (PV), employing building energy management system (BEMS), and switching the central system to gas engine-driven heat pump (GEHP) were analyzed. The reduction of energy consumption due to PCM, PV, BEMS, and GEHP solutions were 2, 6, 23, and 63%, respectively. The use of PCM, PV, and BEMS failed to enabling the building to receive energy label. Instead, the E category energy label was received only by switching the central heating system to the GEHP system. The payback period for switching the facilities to GEHP system lasted 7.5 years. This method of energy labeling can be used for all of the past multi-purpose Buildings.

Identification of energy management configuration concepts from a set of pareto-optimal solutions

Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, selecting a suitable configuration based on multiple, typically conflicting objectives, such as cost, robustness with respect to uncertainty of grid operation, or renewable energy utilization, is a difficult multi-criteria decision making problem. The recently developed concept identification technique can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts).In this process, the partitioning of the objectives and design parameters into different sets (called description spaces) is a very important step. In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions. The data set consists of 20,000 realistic Pareto-optimal building energy management configurations generated by a many-objective evolutionary optimization of a high quality Digital Twin energy management simulator. We analyze how the choice of description spaces, i.e., the partitioning of the objectives and parameters, impacts the type of information that can be extracted. We show that the decision maker can introduce constraints and biases into that process to meet expectations and preferences. The iterative approach presented in this work allows for the generation of valuable insights into trade-offs between specific objectives, and constitutes a powerful and flexible tool to support the decision making process when designing large and complex energy management systems.

Research on Development and Implementation of Integrated Energy Management System for Buildings

Research on Development and Implementation of Integrated Energy Management System for Buildings

Machine learning-based energy monitoring method applied to the HVAC systems electricity demand of an Italian healthcare facility

The buildings energy consumption is a great part of Europe's overall energy demand. The development of diagnostic methods capable of promptly alerting users in case of issues (e.g. mild and progressive decrease in systems components performance) is crucial for the smart management of buildings. Machine learning-based building energy monitoring is a reliable approach for identifying subtle anomalies in the building energy demand behaviour. This study presents the application of a systematic procedure to develop a reliable monitoring method based on machine learning predictive models, ensuring minimal user knowledge requirements. The proposed method applied to the electricity demand of various components of the heating, ventilation and air conditioning system of a real Italian healthcare facility. The obtained models are exploited to apply the building energy monitoring method, assessing its capability to highlight mild changes in building energy demand behaviour. Considering that its application on specific system components implies an increased technical and economic effort to carry out data collection, the present work is aimed at assessing the benefits of such applications. Because of its high reproducibility and relatively simple integration into centralized building energy management systems, the proposed method offers a practical solution to enhance the smart management of building energy systems.

Deep learning-based forecasting of electricity consumption

Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and the building's energy usage. When the dynamics of a building's energy usage are known, it is possible to determine which changes are most likely to reduce consumption. Numerous connected devices, operating modes, energy usage, and environmental factors can all be monitored and controlled in real-time using BEMS. Changing operating times and setting points to maximize comfort and efficiency is made simple by this. In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future forecasting has been done using the LSTM based time series approach. We generated data on the amount of electricity consumed by a hospital facility and tested our suggested methodologies on actual data. The findings gained demonstrated that the strategies were successful with both types of data. On actual data, the trend in electricity consumption can be accurately predicted. Several model optimizers enhanced the suggested methods' performance as well. Our objective function gain accuracy result of 95%.

PROJECT MANAGEMENT STRATEGIES FOR ACCELERATING ENERGY EFFICIENCY IN HVAC SYSTEMS AMIDST CLIMATE CHANGE

Amidst the escalating impacts of climate change, optimizing energy efficiency in Heating, Ventilation, and Air Conditioning (HVAC) systems emerges as a critical imperative. This review explores project management strategies aimed at accelerating energy efficiency in HVAC systems amidst climate change challenges. In response to climate change, the urgency to mitigate energy consumption in HVAC systems has intensified. Project management plays a pivotal role in orchestrating systematic approaches to address this challenge. Firstly, effective project management entails comprehensive planning. This involves conducting thorough energy audits and assessments to identify inefficiencies and prioritize improvement opportunities. Utilizing advanced technologies such as Building Energy Management Systems (BEMS) can enhance data collection and analysis for informed decision-making. Secondly, stakeholder engagement is essential for successful project implementation. Collaboration between building owners, facility managers, engineers, and contractors facilitates alignment of goals and resources. Clear communication channels and regular progress updates foster accountability and momentum throughout the project lifecycle. Thirdly, project management methodologies like Agile or Lean can streamline HVAC efficiency initiatives. Iterative development allows for continuous improvement, enabling teams to adapt to evolving requirements and incorporate feedback effectively. Moreover, risk management strategies are vital to anticipate and mitigate potential obstacles. Contingency plans for supply chain disruptions, regulatory changes, and unforeseen technical challenges ensure project resilience and minimize disruptions. Lastly, monitoring and evaluation mechanisms are indispensable for tracking project performance and verifying energy savings. Utilizing Key Performance Indicators (KPIs) and performance benchmarks enables stakeholders to measure success and adjust strategies as needed. Effective project management strategies are indispensable for accelerating energy efficiency in HVAC systems amidst climate change. By emphasizing comprehensive planning, stakeholder engagement, agile methodologies, risk management, and continuous evaluation, organizations can navigate challenges and realize substantial energy savings in HVAC operations.
 Keywords: Project Management, HVAC, Climate Change, Energy, Efficiency, Review.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Installation and Testing of a Two-Level Model Predictive Control Building Energy Management System

Installation and Testing of a Two-Level Model Predictive Control Building Energy Management System

Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements.

Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.