Energy Consumption In Commercial Buildings Research Articles

МОДЕЛЮВАННЯ ЕНЕРГОЕФЕКТИВНОСТІ ЖИТЛОВОЇ БУДІВЛІ ЗА УМОВ РІЗНИХ ДЖЕРЕЛ ТЕПЛОВОЇ ЕНЕРГІЇ

The paper presents the results of modeling the process of energy consumption in public, residential, and commercial buildings as a result of the use of various sources of thermal energy. Buildings with such energy sources as: a) a low-temperature gas boiler with a capacity of up to 120 kW for the needs of heat supply of a residential building and freon air conditioning systems for the needs of cooling the building; b) a gas condensing boiler with a thermal capacity of up to 120 kW with a temperature regime of 55/45°C for heat supply and freon air conditioning systems for cooling; c) reversible heat pumping unit of the “ground-water” type with a temperature regime of 35/28°C for the needs of the heating and cooling system and a reversible heat pumping machine with a regime of 55/45°C for the needs of hot water supply; d) a reversible air-to-air heat pump for heating and cooling systems and electric boilers for hot water supply; e) a biomass solid fuel boiler with manual fuel loading with a heat output of more than 100 kW for heat supply and freon systems for cooling; f) a fuel pellet boiler with automatic mechanized fuel loading with a heat output of more than 100 kW for building heating and cooling and freon systems for cooling; g) centralized heating system of the building. The results of calculations of the specific primary energy consumption and specific greenhouse gas emissions for each of the above heat sources are presented. It is established that biomass boilers have the lowest values of primary energy consumption and specific greenhouse gas emissions. The lowest efficiency is achieved by a district heating system with a centralized energy source. The modeling was conducted for the climatic conditions of Vinnytsia. At the same time, the disadvantage of using solid fuel boilers is the limitation of their use in densely populated areas, which is explained by the complexity of flue gas cleaning and regular biomass supply. Therefore, it has been determined that the most efficient source of thermal energy for buildings of various types to achieve the standardized energy efficiency indicators is a ground-to-water heat pump system. This installation, along with providing buildings with thermal energy, allows for cooling in the warm season, operating in reverse mode.

Direct Illuminance-Contribution-Based Lighting Control for IoT-Based Lighting Systems in Smart Buildings

With the advent of low-voltage light-emitting diodes (LEDs) and advances in Internet of Things (IoT) technologies, smart buildings have recently become more energy efficient. Nevertheless, the lighting-control system is one of the major sources of electrical energy consumption in commercial buildings. This study proposes a direct illuminance-contribution-based lighting-control framework to reduce the energy of LED luminaires and ensure illuminance for user requirements in smart buildings. Specifically, we designed a direct illuminance-contribution-based lighting-control algorithm (DIC-LCA) using luminaires that are ideally axisymmetric with all light emitted below the horizontal plane and developed a WiFi lighting controller for the IoT-based lighting-control systems in smart buildings. The DIC-LCA can adjust the dimming level by calculating the illuminance based on the line of sight (LOS) distance for energy saving and user satisfaction. After simulation analysis, we prove that energy savings can be achieved by controlling the dimming levels of LED luminaires with high light contribution.

Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones

Abstract This study investigates the impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings across various climate zones. Through a simulation-based approach using the Hourly Analysis Program (HAP), the effectiveness of insulation in reducing energy demand and carbon emissions are evaluated. The research includes multiple climatic regions of Afghanistan, including arid, semi-arid, and mountainous zones. Methodologically, detailed building characteristics, climatic data, and insulation materials are considered, with energy modeling techniques applied to assess the performance of insulation measures. Results indicate varying degrees of energy savings and CO2 emissions reduction associated with insulation across different climate zones, in cities such as Kabul, Herat, Mazar, and Kandahar. Furthermore, the study calculates CO2 emissions reduction resulting from insulation addition, emphasizing the importance of sustainable building practices in mitigating environmental impacts. By underscoring the scientific value of this research in addressing a pressing global challenge and providing actionable insights for building design and energy policy, this study contributes to the advancement of knowledge in the field of sustainable construction and environmental engineering.

Optimizing battery energy storage prototypes for improved resilience in commercial buildings: Gaussian mixture modeling and hierarchical analysis of energy storage potential

The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management.

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

A hybrid deep learning and clonal selection algorithm-based model for commercial building energy consumption prediction.

In contemporary society, commercial buildings, as a crucial component of urban development, face increasingly prominent energy consumption issues, posing significant challenges to the environment and sustainable development. Traditional energy management methods rely on empirical models and rule-based approaches, which suffer from low prediction accuracy and limited applicability. To address these issues, this study proposes a commercial building energy consumption prediction and energy-saving strategy model based on hybrid deep learning and optimization algorithms. This model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and the clonal selection algorithm (CSA), aiming to enhance the accuracy and efficiency of energy consumption predictions. Experimental results demonstrate that the CNN-GRU-CSA Network (CGC-Net) model achieves mean absolute errors (MAE) of 17.12, 16.73, 16.62, and 15.94 on the Building Data Genome Project (BDGP), Commercial Building Energy Consumption Survey (CBECS), Nonresidential Building Energy Performance Benchmark (NEPB), and Building Energy Efficiency Benchmark (BEBDEE) datasets, respectively, significantly outperforming traditional methods and other models. Additionally, the model exhibits faster inference and training times. These results validate the stability and superiority of the CGC-Net model, providing an innovative solution and essential technical support for commercial building energy management.

Assessing the efficacy of green building design strategies in minimizing energy consumption in commercial buildings of Mumbai: A building performance analysis

This research paper investigates the effectiveness of green building design strategies in minimizing energy consumption in commercial buildings in the bustling metropolis of Mumbai, India. With a rapidly urbanizing and densely populated environment, Mumbai presents a unique setting for assessing green building practices. The research objectives focus on understanding the impact of various green building design features, such as green roofs, solar panels, and floor area optimization, on energy efficiency in commercial structures. The research employs a robust methodology, incorporating regression analysis to analyze data obtained from the Mumbai Municipal Corporation's building energy consumption records. Through this analysis, the study investigates the relationships between green building design parameters, building characteristics, and energy consumption patterns. Key findings indicate that specific green building features, including intensive and biodiverse green roofs and solar panel installations, significantly reduce energy consumption. Additionally, older buildings tend to exhibit lower energy consumption, emphasizing the need for retrofitting existing structures with energy-efficient solutions. The study also underscores the role of occupant density in influencing energy efficiency, with higher occupant rates leading to more efficient space utilization and energy use. The implications of this research are far-reaching, offering insights into tailoring green building strategies to the specific challenges posed by densely populated urban areas. These findings hold significance for sustainable urban development, policy formulation, architectural design, and the global effort to mitigate climate change.

A multi-task deep reinforcement learning-based recommender system for co-optimizing energy, comfort, and air quality in commercial buildings with humans-in-the-loop

Abstract We introduce a novel human-centric deep reinforcement learning recommender system designed to co-optimize energy consumption, thermal comfort, and air quality in commercial buildings. Existing approaches typically optimize these objectives separately or focus solely on controlling energy-consuming building resources without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning with humans-in-the-loop and demonstrate how it can jointly learn energy savings, comfort, and air quality improvements for different building and occupant actions. In addition to controlling typical building resources (e.g., thermostat setpoint), our system provides real-time actionable recommendations that occupants can take (e.g., move to a new location) to co-optimize energy, comfort, and air quality. Through real deployments across multiple commercial buildings, we show that our multitask deep reinforcement learning recommender system has the potential to reduce energy consumption by up to 8% in energy-focused optimization, improve all objectives by 5–10% in joint optimization, and improve thermal comfort by up to 21% in comfort and air quality-focused optimization compared to existing solutions.

Optimal Supply-Side and Demand-Side Management Strategies for Energy Efficiency in Residential Buildings using Particle Swarm Optimization

The global energy consumption of commercial and residential buildings, driven by economic and demographic growth, has become a critical concern. This surge in demand has resulted in elevated electricity prices, strain on the primary grid, and increased carbon emissions due to inefficient energy utilization. This paper introduces an innovative energy management system (EMS) integrating both supply-side (SSM) and demand-side management (DSM) strategies. The demand side management involves optimizing load scheduling based on predicted electricity pricing. In contrast, the supply side management determines the next 24 hours optimal power setpoints, incorporating a Photovoltaic System (PV), an Energy Storage System (ESS), and the Electrical Power Grid (EPG). The primary objectives of this proposed EMS are to concurrently decrease electricity costs and the peak-to-average ratio (PAR), all while ensuring user comfort in terms of appliance operating waiting times. The proposed multi-objective optimization problem is solved using Particle Swarm Optimization algorithm (PSO). Based on residential building energy demand and time-of-use pricing (TOU), simulations were conducted using MATLAB. The results demonstrate the effectiveness of the proposed approach, achieving a 28% reduction in both electricity costs and the PAR, while maintaining user comfort.

Occupant-centric metadata paradigms for comfort optimization in buildings

Three decades of building performance standards have resulted in a meaningful reduction in energy consumption in commercial buildings, but limited improvement in thermal comfort has been observed. ASHRAE Standard 55, the primary reference used to design and operate “comfortable” buildings, specifies six thermodynamic factors that must be addressed to provide comfort in a space. In practice, survey data reports only a small percentage of buildings are considered satisfactory by occupants. We perform a literature review to demonstrate that the range of each of the six thermodynamic dimensions of comfort can be observed well outside of the applicability of ASHRAE 55 in real buildings. The typical practice of modulating air temperature (Ta ) is shown to result in frequent occupant dissatisfaction. We posit that to be able to more consistently operate within a given “comfort zone” in a wider range of thermodynamic environments, we must embed within building systems higher-dimensional, occupant-centric parameters. To understand if two popular metadata schemas, Project Haystack and Brick Schema, are equipped to support higher-dimensional control paradigms, we (1) define a model, measure, manipulate (M3) evaluation criteria, which we (2) apply to each of the above schemas to determine if they can be used to codify closed-loop control for a given set of thermodynamic dimensions of comfort. We conclude that recent extensions to the Brick Schema provide much of the background necessary to codify closed control loops in six dimensions, and that Project Haystack requires a greater expansion.

Energy Consumption, Energy Analysis, and Solar Energy Integration for Commercial Building Restaurants

In the domain of energy consumption in restaurant-type commercial buildings, traditional energy audits tend to concentrate mainly on electrical loads, often neglecting the specifics of the restaurant sector, especially regarding liquified petroleum gas fuel consumption. This research employs a comprehensive energy audit framework specifically designed for the commercial building restaurant sector. Using energy data from 130 restaurants, we computed the building energy index that ranged in between 650 and 1000 kWh/m2/year. Using linear regression, we assessed the relationship between building energy index and restaurant area, uncovering a low R2 value, suggesting the unsuitability of the building energy index as an exclusive measure for restaurants. Concurrently, our detailed comparative study showed that liquified petroleum gas-fueled equipment uses about 38% more energy than electric fueled equipment but is 0.5% cheaper and significantly less polluting. Investigating renewable energy potentials, we found solar PV application as a viable option for restaurants. The results showed that solar PV installation could produce approximately 11,064,898 kWh, translating to utility savings of RM 7,381,929 and reductions of 7,108,327 kgCO2, 68,959 kgSO2, and 31,823 kgCO emissions. Conclusively, our findings underline the need for a diversified energy assessment in restaurants and the tangible benefits of renewable energy integration.

Identifying salient features of cooling energy usage of commercial buildings using explainable artificial intelligence

ABSTRACT Electrical energy usage of commercial buildings (over 60% in the USA) is of growing concern due to its impact on the environment. To reduce it, it is necessary to identify the factors contributing to commercial building energy usage. Towards this, we studied the importance of various building features and their contributions on cooling energy, using two datasets of Commercial Building Energy Consumption Survey (CBECS) 2018 and 2012. For this, Shapash, an explainable artificial intelligence technique, was used, with Random Forest, to predict the cooling energy usage intensity (EUI). The two most important features for 2018 are cooling degree days and principal building activity, while cooling degree days and cooling percentage were for 2012 with 54.79% and 33.46% contributions of the total consumption respectively. The comparison of feature importance indicated that cooling degree days are the topmost important feature impacting cooling EUI and have a more significant contribution of 34.29% for 2018 and 19.68% for 2012. Overall, the most important features impacting cooling EUI were cooling degree days, principle building activity, cooling percentage and total hours open per week. The results from this study provide insights into the most significant factors influencing energy consumption and help in developing strategies for reducing it.

Expertise-based systematic guidelines for chiller retrofitting in healthcare facilities

Air Conditioning and Mechanical Ventilation (ACMV) generally consume more than half of the energy consumption in commercial buildings in a tropical region. In healthcare facilities, the ACMV systems must comply with the following crucial parameters: indoor air quality, which greatly impacts the thermal comfort of occupants, as well as pressure regime regulations and standards, particularly in critical areas. Due to the high cost of operating the ACMV system, many health facilities in Malaysia have embarked on energy efficiency initiatives. Many have replaced their chillers with magnetic-bearing chiller compressors to benefit from cost reduction. However, despite the replacement, it was found that not all healthcare facilities benefited fully from improved air condition systems other than the higher chillers' coefficient of performance obtained from the new installation. The paper's main contribution is to offer a comprehensive and systematic guideline for facility managers to evaluate healthcare facilities ACMV systems and provides solutions that match the healthcare facilities ventilation requirements standards in making effective decision before embarking chiller retrofitting. The proposed guideline is based on the experience and observation of the expertise through energy auditing of the ACMV system and chiller retrofitting evaluation at the healthcare facilities in Malaysia. It may contribute towards more cost-effective management of ACMV system at healthcare facilities in Malaysia while assisting facility managers on chiller retrofitting decision making. More importantly, the guideline has the potential to support sustainable performance of the ACMV system.

Predicting the Energy Consumption of Commercial Buildings Based on Deep Forest Model and Its Interpretability

Building energy assessment models are considered to be one of the most informative methods in building energy efficiency design, and most of the current building energy assessment models have been developed based on machine learning algorithms. Deep learning models have proved their effectiveness in fields such as image and fault detection. This paper proposes a deep learning energy assessment framework with interpretability to support building energy efficiency design. The proposed framework is validated using the Commercial Building Energy Consumption Survey dataset, and the results show that the wrapper feature selection method (Sequential Forward Generation) significantly improves the performance of deep learning and machine learning models compared with the filtered (Mutual Information) and embedded (Least Absolute Shrinkage and Selection Operator) feature selection algorithms. Moreover, the Deep Forest model has an R2 of 0.90 and outperforms the Deep Multilayer Perceptron, the Convolutional Neural Network, the Backpropagation Neural Network, and the Radial Basis Function Network in terms of prediction performance. In addition, the model interpretability results reveal how the features affect the prediction results and the contribution of the features to the energy consumption in a single building sample. This study helps building energy designers assess the energy consumption of new buildings and develop improvement measures.

PREDICTING ENERGY USE INTENSITY OF US HOTEL BUILDINGS USING CBECS MICRODATA

In the United States, the commercial sector consumes 21% of the total energy use. With 14%, the ~47,000 hotels are considered to be the third main source of energy consumption in the commercial sector. Stakeholders in the hotel industry have shown significant interest in reducing energy consumption in hotel buildings. However, determining the primary factors that contribute to overall energy consumption is important to develop efficient and effective retrofitting strategies. To address this question, this study focuses on identifying the variables in hotels that contribute majorly to their energy consumption. To achieve that, this study utilizes different machine learning approaches for estimating the source of Energy Use Intensity (EUI) for US hotel buildings based on Commercial Building Energy Consumption Survey (CBECS) 2018 microdata. The findings derived from this research can significantly contribute to the optimization of retrofitting strategies and building design in hotel buildings, as well as the development of effective electrification strategies. Ultimately, this knowledge will empower decision makers to make informed choices that enhance energy efficiency and sustainability in hotel buildings.

PREDICTING ENERGY USE INTENSITY OF US HOTEL BUILDINGS USING CBECS MICRODATA

In the United States, the commercial sector consumes 21% of the total energy use. With 14%, the ~47,000 hotels are considered to be the third main source of energy consumption in the commercial sector. Stakeholders in the hotel industry have shown significant interest in reducing energy consumption in hotel buildings. However, determining the primary factors that contribute to overall energy consumption is important to develop efficient and effective retrofitting strategies. To address this question, this study focuses on identifying the variables in hotels that contribute majorly to their energy consumption. To achieve that, this study utilizes different machine learning approaches for estimating the source of Energy Use Intensity (EUI) for US hotel buildings based on Commercial Building Energy Consumption Survey (CBECS) 2018 microdata. The findings derived from this research can significantly contribute to the optimization of retrofitting strategies and building design in hotel buildings, as well as the development of effective electrification strategies. Ultimately, this knowledge will empower decision makers to make informed choices that enhance energy efficiency and sustainability in hotel buildings.

HVAC Systems Evaluation and Selection for Sustainable Office Buildings: An Integrated MCDM Approach

Heating, Ventilation, and Air-Conditioning (HVAC) systems are critical components of maintaining an indoor air quality that ensures the thermal comfort of occupants in diverse building types. However, HVAC systems are also responsible for a substantial portion of the total energy consumption of commercial and industrial office buildings. This paper presents an integrated approach of two powerful MCDM techniques: the Best-Worst-Method (BWM) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to evaluate and rank HVAC systems to ensure the best selection toward designing a sustainable office building. A set of conflicting criteria reported by international sustainable assessment centers were employed along with various HVAC systems to develop what is called herein a BWM-based TOPSIS model in order to conduct such a novel research attempt. Within the context of the investigated office building herein, the mini-package ductless system appears to be the best choice. This study could be further enhanced by including additional criteria and a larger sample size in future studies.

Predicting Commercial Building Energy Consumption Using a Multivariate Multilayered Long-Short Term Memory Time-Series Model

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most commonly used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate, multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE of 30%, RMSE of 25%, and MAPE of 20% compared with the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE of 10%, RMSE of 20%, and MAPE of 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%.These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

A novel and lean data-based method to calculate the actual HVAC zone energy consumption and cooling load in sustainable smart cities using a single temperature sensor

Sustainability and energy savings are two intertwined research aims for humanity’s long-term survival, aligned with the Paris Agreement and the Egyptian vision of 2030. The purpose of this research is to minimize the cost needed to measure the HVAC energy consumption in educational and commercial buildings to save energy for energy efficiency strategies to serve the future smart and sustainable cities. These facilities are among the most energy-consuming loads, and this research proposes a technique to determine real HVAC energy consumption and cooling load using a single-sensor method. Calculating the as-built HVAC energy usage and real cooling load, especially in complex HVAC systems like VRV, which are some of the most challenging gaps, is achieved through a revolutionary and cost-effective approach. The proposed approach computes the actual HVAC energy consumption of each specific zone in the building using data from a single temperature sensor. Furthermore, the real and instantaneous cooling load of each zone was calculated, and the results show a promising agreement between the proposed techniques and the real measured consumption of the HVAC system. This approach can be integrated into the BMS building management system to determine the actual HVAC energy usage and cooling load for each zone individually in real time, enabling significant energy reduction decisions with optimal cost efficiency. Furthermore, it can be used by the BMS system as a real-time On-Board Diagnostic of the HVAC system.

Exploring spatial and environmental heterogeneity affecting energy consumption in commercial buildings using machine learning

Building energy consumption (BEC) is a critical indicator for promoting sustainable building development. However, previous studies have neglected the spatial heterogeneity of the urban environment and its landscape configuration on BEC impacts. This paper aims to identify the factors influencing commercial building energy consumption in Singapore, including building basic information, Green Mark certification, usage behaviours, and urban environment. We employed geographically weighted regression models to analyse influencing factors, K-means to classify clusters, and artificial neural networks for prediction. Our approach yielded a significant improvement in the fitting effect (by 38%) compared to traditional regression algorithms. Our findings showed a strong correlation between BEC and geographical information and economic development. Building basic information was the most influential aspect, with building coverage area being the most dominant driving force with positive impacts, reaching a mean regression coefficient of 0.423. Land surface temperature had the most potent negative effect on BEC among urban factors, while water area ratio and gross plot ratio had a strong non-stationary influence on BEC. Our study provides insights into the evolving and heterogeneous nature of the urban environment, supporting decision-making for more sustainable metropolitan development that benefits both people and nature.