Energy Consumption In Commercial Buildings Research Articles

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.

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.

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.

Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods

Building energy forecasting methodologies utilized by municipal governments tend to be geared heavily towards depicting broader qualitative representations of regional change and are in need of complementary data-driven models that can produce quantitatively reliable depictions of future energy consumption at the neighborhood-level. The current research demonstrates an application of a Machine Learning (ML) model in the form of an Extreme Gradient Boosting (XGBoost) algorithm for forecasting the energy use of commercial and residential buildings. The methodology serves to improve on municipal scenario planning by providing a more spatially granular representation of future energy use. In this way, city government and urban planners can more accurately set carbon emission benchmarks and target specific locales for sustainability initiatives. The second major contribution of the study is to demonstrate how scenario planning approaches can utilize existing Machine Learning techniques to compensate for gaps in the data. This work is developed through a case study of Philadelphia. The study begins with the construction of residential and commercial energy models for the year 2015 and corresponding models for the year 2045. The forecast models integrate regional socioeconomic trends from the Delaware Valley Regional Planning Commission (DVRPC) scenario of Enduring Urbanism. The commercial energy use model is developed from the DVRPC’s open-source Geographic Information System (GIS) datasets, the Commercial Buildings Energy Consumption Survey (CBECS), and CoStar commercial real estate data. The residential model applies the Residential Energy Consumption Survey (RECS), the Public Use Microdata Sample (PUMS), and Census Bureau American Community Survey (ACS) estimates. A corresponding SHAP (SHapley Additive exPlanations) analysis is implemented to pinpoint feature contributions to the model’s energy estimates. By using the PopGen software, the model’s energy estimates could be analyzed at the household level, the smallest possible scale. To provide a useful resource for key stakeholders, the study aggregates model output by Traffic Analysis Zone (TAZ) and Public Use Microdata Area (PUMA) to display a detailed forecast of energy use. The results indicate that the DVRPC Enduring Urbanism trends in income and employment do not significantly affect energy consumption for the study area. However, features related to lower building intensity (e.g., lower square footage, fewer floors per building) were associated with reduced energy use in both models. Additionally, the study found residential buildings under the “single-family attached” zoning designation to correspond with higher energy estimates.

A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption

Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R2 between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications.

The impact of energy shortages on health and healthcare in Europe.

The impact of energy shortages on health and healthcare in Europe.

Comparative Analysis of Data-Driven Techniques to Predict Heating and Cooling Energy Requirements of Poultry Buildings

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings’ energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model’s effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions.

Smart Energy Consumption Control in Commercial Buildings Using Machine Learning and IOT

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.