Efficient Building Energy Management Research Articles

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.

Enhancing building energy efficiency: An integrated approach to predicting heating and cooling loads using machine learning and optimization algorithms

Predicting cooling and heating loads is essential for efficient building energy management in order to maintain indoor comfort. This study employs machine learning methods containing Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and a Dempster-Shafer Theory-based ensemble model using a dataset of 768 samples. 5-fold cross-validation selects 80 % of the dataset for training, with optimization algorithms like Coot, Dingo, and Sea-Horse Optimizers fine-tuning model applicability. Evaluation metrics such as R2, RMSE, n10-index, MARE, SI, WAPE, and NSE assess model performance. XGB outperforms SVR in heating and cooling load prediction. The XGSH model, combining XGB with the Sea-Horse Optimizer, obtains the highest performance, with an RMSE of 0.9729 and R2 of 99.17 % for heating load (HL) estimation. Similarly, for cooling load (CL) estimation, the XGSH model shows the highest R2 (99.36 %) and lowest RMSE (0.7723) during training. The results suggest practical usability for energy management and indoor comfort maintenance, benefiting engineers and decision-makers alike.

건물 에너지의 효율적 관리를 위한 저해상도 열화상 이미지를 활용한 재실자 수 예측에 관한 연구

건물 에너지의 효율적 관리를 위한 저해상도 열화상 이미지를 활용한 재실자 수 예측에 관한 연구

Digital twins for smart building at the facility management stage: a systematic review of enablers, applications and challenges

PurposeDespite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.Design/methodology/approachA systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.FindingsThe study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.Practical implicationsThe findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.Originality/valueThis study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

Energy consumption disaggregation in commercial buildings: a time series decomposition approach

As commonly stated, we cannot manage what we do not measure. Understanding the flow of energy and its end-uses within a building is critical for energy management. Therefore, the lack of high resolution energy submetering is a significant barrier to efficient energy management in buildings. Despite this, many buildings still lack adequate submetering for their major end-uses because of the cost and practical restrictions. Energy disaggregation techniques aim at breaking down the bulk meter energy data into primary end-uses to gain insight into consumption patterns. However, high resolution, trustworthy BAS trend data is essential to develop reliable disaggregation techniques and capture unmeasured energy flow accurately. This paper explores a time series decomposition based method to disaggregate the total energy use into three major end uses namely lighting and plug loads, cooling, and heating energy use without BAS trend data. The results were compared with actual submetered data from ten office buildings in Ottawa, Canada for validation purposes. Specific insights into lighting and thermal scheduling, as well as hourly, daily, and monthly operational variations based on the de-composition components were discussed. The promising performance of the proposed method suggests that it could be used for quick and low cost auditing of commercial buildings with access to only the building’s total energy use data.

Correlation analysis of building parameters according to ASHRAE Standard 90.1

Building energy codes promote efficient energy management in both conventional and eco-friendly buildings. These codes are periodically updated to reduce energy consumption and carbon emissions. However, these updates can induce correlations among building parameters, which could lead to incorrect results when creating or analyzing data sets based on them. This study aims to investigate the impact of correlations in building parameters caused by the trend of updates in building energy codes. The most widely used building energy standard, namely Standard 90.1 of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), is utilized to understand the trends of energy codes. We confirm that periodic updates to ASHRAE Standard 90.1, representative of numerous energy standards and codes, induce correlations in the input parameters. Moreover, we analyze the problems caused by these correlations through sensitivity analysis and Principal Component Analysis (PCA). Consequently, the sensitivity index of parameters with correlations increases while the importance of parameters without correlations decreases. In the PCA, the number of parameters required to explain more than 95 % of the variance in the raw data changed. This study's findings highlight the need to carefully consider correlations when analyzing building energy code-impacted data and provide insight that enhances the accuracy of the interpreting parameter relation in such data analysis.

Ti3C2Tx MXene/delignified wood supported flame-retardant phase-change composites with superior solar-thermal conversion efficiency and highly electromagnetic interference shielding for efficient thermal management

The development of form-stable phase-change material (PCM) with solar-thermal harvest, electromagnetic interference (EMI) shielding, and flame-retardancy is crucial for efficient building energy management and conservation. Herein, novel Ti3C2Tx MXene/delignified wood supported form-stable phase-change composites (PMPCMs) with superior solar-thermal conversion efficiency, highly EMI shielding effects, and excellent flame-retardancy were fabricated by alkaline boiling delignification, ammonium dihydrogen phosphate (ADP) and MXene deposition, and melted n-octadecane impregnation. Benefiting from the powerful surface tension and capillary force of the wood-based support materials, PMPCMs can effectively encapsulate the melted n-alkane molecules and inhibit their leakage. The differential scanning calorimetry results showed that PMPCMs possessed superior n-octadecane encapsulation yield and high thermal energy storage density (166.9–191.6 J/g). Decorating delignified wood by MXene significantly improved the solar-thermal storage efficiency (up to 88.4 %) and EMI shielding performance of PMPCMs. The average EMI shielding effectiveness of PMPCM-10 was enhanced to 34.12 dB in X-band frequency range of 8.2–12.4 GHz. Moreover, with the introduction of the flame-retardant ADP, the peak heat release rate (pHRR) and total heat release (THR) of PMPCMs decreased noticeably, demonstrating the enhanced flame retardancy of delignified wood supported phase-change composites. In summary, PMPCMs exhibited superior application prospects in building energy management and electromagnetic shielding fields.

An IMU dataset for human thermal comfort activities identification: Experimental designs and applications

Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use. Through analyzing inertial measurement units (IMU) data from wearable devices with machine learning, thermal comfort of occupants can be detected in a non- intrusive method. This paper proposed a dataset consisted of IMU data collected from 30 participants (14 males and 16 females, aged 23.23 ± 1.70 years, height 168.67 ± 8.02 cm, and weight 59.55 ± 10.96 kg) who wore two IMUs on their hands while performing 30 thermal comfort activities (10 cold-related, 10 hot-related, and 10 neutral activities) according to their personal habits.The database is divided into two parts: (1) Single activities data, which includes 4500 samples acquired from experiments where each participant was asked to perform 30 thermal comfort activities individually. (2) Continuous multi-activity data, which comprise 360 samples collected while participants performed a series of randomly assigned activities in a more natural and continuous manner. The combination of these two parts provides a comprehensive dataset for both the training and testing phases of machine learning models. By offering detailed labels, this database aims to serve as a foundation for research exploring machine learning applications in detecting occupant thermal comfort, ultimately contributing to improved indoor environments and more efficient building energy management.

Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture

Accurate building energy consumption forecasting is crucial for developing efficient building energy management systems, improving energy efficiency, and local building energy supervision and management. However, short-term building energy consumption forecasting is challenging due to highly non-smooth and volatile trends. In this paper, we present a novel methodology that combines interpretable decomposition methods with an interpretable forecasting model. We first illustrate a daily energy consumption pattern recognition (DECPR) method, which decomposes daily energy consumption patterns into interpretable energy consumption subsequences. To achieve satisfactory forecasting performance, we design the vector representation of each subsequence as a static input to the temporal fusion transformers (TFT) model. This vector representation integrates the DECPR method into the TFT model. The TFT model produces interpretable outputs, such as the attention analysis of different step lengths and the visualization of the importance ranking of exogenous variables, including meteorological data, calendar information, and the vector representation. Empirical studies demonstrate that our proposed DECPR-TFT system outperforms comparable models with a mean absolute percentage error (MAPE) of 6.11%, which is significantly lower than other models. These interpretable outputs provide valuable insights for researchers seeking to develop energy-saving operation strategies in buildings. Overall, our methodology offers a promising solution for short-term building energy consumption forecasting that can contribute to more efficient building energy management and energy-saving operation strategies.

DIGITAL TWIN SOLUTION IMPLEMENTED ON ENERGY HUB TO FOSTER SUSTAINABLE SMART ENERGY CITY, CASE STUDY OF SUSTAINABLE SMART ENERGY HUB

Abstract. The scarcity of energy is one of humanity's most pressing issues in the twenty-first century. These problems can be found in various areas, including energy supply, exchange, and consumption. Population growth, rising global energy demand, natural resource shortages, and environmental concerns contribute to energy scarcity. Furthermore, energy shortage needs the expansion of renewable energies and energy efficiency, a major concern for all governments and organisations. Building energy efficiency is a crucial concept to consider when it comes to smart cities. Buildings are the greatest energy consumers, accounting for 40% of total energy consumption. Recent advances in machine learning within a big data context have paved the way for more efficient building energy management. Building energy consumption may be successfully managed by a real-time measurement procedure, allowing the economy to shift from a linear to a circular consumption model. This will tackle the issue of late notice of failed energy-saving initiatives and allow for immediate correction to restore the energy management system to ultimate performance.The energy hub might be as small as a single home energy system or as large as an energy system for the city. This paper will present a case study on developing a smart energy hub called Hubgrade 4.0 that relies on connected products using digital twin as a significant enabler for energy-saving initiatives. Hubgrade 4.0 provides an innovative approach to successfully implementing energy efficiency improvement using artificial intelligence and real-time data. Hubgrade 4.0 is the name of Enova by Veolia smart energy hub; Enova is the regional leader in integrated energy and multi-technical services, delivering performance-based energy and facilities management solutions. The energy hub digital twin is a link between a physical platform that administers the energy hub's IoT and a virtual platform that can derive services that are valid for the energy hub. Successful enterprises are using several new technologies to achieve the goals of Industry 4.0: efficiency, speed, agility, and customer-centricity. This paper concluded and highlighted lessons learned from the successful implementation of innovative energy management, which relied on a dedicated organisation, effective adoption of digital technologies, and embracing new business models, resulting in power savings of 254 million kW and water savings of around 3 million cubic meters, as well as financial savings of about 138 million AED in 5 years since Hubgrade 4.0 started operations its first energy-saving contract in 2017.

A short-term building energy consumption prediction and diagnosis using deep learning algorithms

Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation.

Guidelines for efficient and sustainable energy management in hospital buildings

The increasing global consumption of energy and its negative impacts on the environment has been an issue discussed both by academics and practitioners, including in the health sector. Hospitals are major consumers of energy, which leads to increased operating costs, decreased competitiveness, and negative environmental impacts. Energy management in hospitals must be understood as a way to contribute to environmental sustainability and as a way to improve competitiveness. Therefore, the objective of this paper is to propose guidelines for efficient and sustainable energy management in hospitals, developed based on the literature review, the challenges faced and the good practices of sustainable energy management in the hospitals studied, the cross analysis of the case studies, and the authors' expertise. As a result, the eight proposed guidelines are related to culture change, energy consumption management, energy management standard, purchase of renewable energy, green technologies, sustainable hospital buildings, social responsibility programs focused on energy use, and sustainable energy public policies. These guidelines contribute in an applied/managerial way to help managers of public and private hospitals overcome the challenges of energy management and contribute to UN's SDG7 due to its potential to improve energy efficiency and environmental performance, therefore, being a movement towards clean energy. This study also theoretically/scientifically contributes to the academic debate since the empirical findings found in the hospitals studied and the proposition of guidelines deepen and expand the key elements identified in the literature on efficient energy management in hospitals, providing the basis for developing new research on the subject.

Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.

A scalable platform for investigating the space-specific features of the temporal energy usage pattern and saving potential with real-time bigdata

The Korean government has initiated a “Green New Deal” policy, in which a smart energy platform was proposed for more efficient building energy management that could monitor energy consumption in real time and control electrical installation and appliance in a timely manner. With the energy bigdata collected via IoT-based smart energy meter, this study aimed to develop a scalable platform for investigating the space-specific features of the temporal energy usage pattern and the relevant energy saving potential by electrical installation (i.e., lighting, electrical appliances, heating & cooling systems) in a building. This study was conducted in four stages: (i) real-time bigdata collection; (ii) development of dashboard; (iii) scalable analysis of energy usage pattern; and (iv) scalable analysis of energy saving potential. Main finding can be summarized as follow. First, more than 50% of the energy usage in main spaces (i.e., classrooms) was consumed by the heating & cooling systems. Second, the total energy saving potential was estimated at 11.50% when applying the developed actuator to the standby power and electrical radiator. Third, the annual cost saving potential as economic feasibility was estimated at 11.13%. The proposed scalable platform can contribute to investigating the energy usage pattern by electrical installation from a spatio-temporal perspective and to changing the occupants’ behaviors for energy savings by considering space-specific features and occupancy patterns. The proposed novel approach can be extended to various types of buildings.

The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Aiming at the current situation of complicated energy-consuming equipment and large energy consumption in the current enterprise, a reference model scheme of an enterprise energy efficiency management platform based on the Internet of Things is analyzed and the dynamically collected energy consumption information is analyzed using the Internet of Things technology and data mining technology. They provide decision-making support for enterprise energy efficiency management decisions and formulation of energy-saving and emission reduction plans. This article analyzes the current status of energy efficiency management in buildings and points out several pain points in energy efficiency management, including installation difficulties, high software development costs, upgrade difficulties, long debugging cycles, and closed systems, and introduces intelligent power distribution monitoring based on the Internet of Things technology to solve these difficulties. In the system, we analyze the actual value brought by energy efficiency management technology through case studies. It can collect scattered information through the Internet of Things, gather it together to form massive data, and obtain the corresponding information through data analysis and processing. The design is reasonable, the practicality is strong, the use effect is good, and it is easy to promote and use.

Extremum seeking‐based observer design for reduced order models of coupled thermal and fluid systems

SummaryWe present an extremum seeking (ES)‐based robust observer design for thermal‐fluid systems, pursuing an application to efficient energy management in buildings. The model is originally described by Boussinesq equations which is given by a system of two coupled partial differential equations (PDEs) for the velocity field and temperature profile constrained to incompressible flow. Using proper orthogonal decomposition, the PDEs are reduced to a set of nonlinear ordinary differential equations. Given a set of temperature and velocity point measurements, a nonlinear state observer is designed to reconstruct the entire state under the error of initial states, and model parametric uncertainties. We prove that the closed loop system for the observer error state satisfies an estimate of L2 norm in a sense of locally input‐to‐state stability with respect to parameter uncertainties. Moreover, the uncertain parameters estimate used in the designed observer are optimized through iterations of a data‐driven ES algorithm. Numerical simulation of a two‐dimensional Boussinesq PDE illustrates the performance of the proposed adaptive estimation method.

Business Processes and Comfort Demand for Energy Flexibility Analysis in Buildings

Occupant behavior and business processes in a building environment constitute an inseparable set of important factors that drives energy consumption. Existing methodologies for building energy management lag behind in addressing these core parameters by focusing explicitly on the building’s structural components. Additional layers of information regarding indoor and outdoor environmental conditions and occupant behavior patterns, mostly driven by everyday business processes (schedules, loads, and specific business activities related to occupancy patterns and building operations), are necessary for the effective and efficient modeling of building energy performance in order to establish a holistic energy efficiency management framework. The aim of this paper was to develop a context-driven framework in which multiple levels of information regarding occupant behavior patterns resulting from everyday business processes were incorporated for efficient energy management in buildings. A preliminary framework evaluation was performed in a multifaceted university building involving a number of spaces, employees, business processes, and data from sensors and metering devices. The results derived by linking operational aspects and environmental conditions (temperature, humidity, and luminance) to occupant behavior underlying business processes and organizational structures indicated the potential energy savings: a max of 7.08% for Heating, ventilation, and air conditioning (HVAC), 19.46% for lighting and a maximum of 6.34% saving related to office appliances.

Estimating thermal parameters of a commercial building: A meta-heuristic approach

The present study proposes a novel methodology based on a meta-heuristic algorithm to accurately estimate the thermal parameters of a building. These parameters are often most difficult to be quantified in practice as these are strictly determined by the characteristics of the materials used in building construction, which vary significantly from one building structure/type to another. The parameter estimation problem is formulated as a single-objective optimization problem and the thermal model of the building has been developed in EnergyPlusTM, following black-box identification strategy. Performance of the proposed approach is demonstrated considering both practical and simulated energy consumption data of the building, after subjecting the thermal model to two different geographical environments of New Zealand, with significant weather profile variations. The results of the investigation illustrate that the proposed methodology could effectively estimate the building thermal parameters. This approach, therefore, will facilitate in developing efficient Building Energy Management Systems (BEMS).

Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.

Modeling of Room Temperature Dynamics for Efficient Building Energy Management

Heating, ventilating and air-conditioning systems have a significant share in the energy consumed by buildings. Modeling of room temperature dynamics is the first-step in designing an efficient air-conditioning system. In this regard, this paper proposes a semi-nonlinear thermal model: ordinary differential equation, with parameters as nonlinear functions of ambient temperature and cooling air flow-rate. To validate the performance of the model, a three-roomed building, equipped with an air-conditioning system is modeled, and Navier–Stokes equations are solved to simulate the temporal evolution of temperatures for different ambient temperatures and cooling air flow-rates. The steady-state temperature and transient solution parameters of the thermal model are assumed to be polynomial functions of ambient temperature and flow-rate, and are determined by minimizing the errors between the thermal model- and the computational fluid dynamics-based solutions of final and transient temperatures, respectively. The proposed thermal model of third-order and nonlinear type transient coefficients is shown to predict the temporal evolution of temperature accurately. Further increase in prediction accuracy is achieved by recursively updating the parameters online using extended Kalman filter. The high prediction accuracy of the proposed thermal model makes it a potential candidate for the design of an optimal temperature regulator.