Energy Consumption In Commercial Buildings Research Articles

Feasibility study of the application of a cooling energy storage system in a chiller plant of an office building located in Santiago, Chile

Energy consumption of commercial buildings has increasingly gained attention worldwide, because of its significant electricity consumption and peak power demand. Chiller plants are mostly used in commercial buildings to generate cold water for air conditioning. However, because the chiller plants and the refrigeration systems tend to be oversized with respect to their critical design load conditions, they certainly lead to higher energy consumption than a properly sized chiller plant. One possible way to reduce the power consumption and redistribute energy use is through the integration of latent heat thermal energy storage (LHTES) systems with air-cooling system in buildings. In the present work, a LHTES system based on ice is implemented along with a conventional chiller system of an existing commercial building located in Santiago, Chile. Different strategies of operation in a design day are assessed by computational simulation with EnergyPlus. A hybrid operation strategy is evaluated for the hottest summer week, and the results of the cooling loads are obtained. Finally, we founded that the implementation of a LHTES reduces by 7.8% the cooling energy consumption of the cooling system during the analyzed period.

Modeling of Building Energy Consumption for Accommodation Buildings (Lodging Sector) in Japan—Case Study

The energy performance of residential and commercial buildings is a vital topic because of the rapid urbanization occurring throughout the world. Accommodation buildings are considered energy intensive compared to other commercial facilities. In addition, they are the main component of the tourism industry. Therefore, various actions and policies have been introduced to improve the energy performance of accommodation buildings. This research depends on a national scale database of energy consumption of commercial buildings in Japan. It is the main source of data to conduct this study. The Database for Energy Consumption of Commercial buildings (DECC) is a national survey that is provided by the Japan Sustainable Building Consortium (JSBC). Based on the DECC, the study presents a general benchmark which is developed by applying regression and artificial neural network (ANN) methods to assess the energy performance of accommodation buildings in the Kanto region, Japan. The study presents a broad benchmark to evaluate basic energy consumption of accommodation building with three variables. In addition, the study highlights the necessity of designing the ANN model through the choosing of hidden layers and training method. The outcomes of each learning method and hidden layer was examined using main indicators to verify its accuracy.

Real-time prediction model for indoor temperature in a commercial building

Indoor environmental parameters have significant influence on commercial building energy consumption and indoor thermal comfort. Prediction of these parameters, especially that of indoor air temperature, along with continuous monitoring and control of real world parameters can aid in the management of energy consumption and thermal comfort levels in existing buildings. An accurate indoor temperature prediction model assists in achieving an effective energy management strategy such as resetting air temperature set-points in commercial buildings. This study examines the real indoor environmental data for multiple adjacent zones in a commercial building in the context of thermal comfort and identifies the possibility of resetting air temperature set-point without compromising the occupant comfort level. Also, the value of predicting the indoor temperature accurately in such a building is established through this case study. This study presents a nonlinear autoregressive network with exogenous inputs-based system identification method to predict indoor temperature. During model development efforts have been paid to optimize the performance of the model in terms of complexity, prediction results and ease of application to a real system. The performance of single-zone and multi-zone prediction models is evaluated using different combinations and sizes of training data-sets. This study confirms that evaluating the performance of the model in the context of major contributing aspects such as optimal input parameters and network size, optimum size of training data, etc. offers optimized model performance. Thus, when the developed model is used for long-term prediction, it provides better prediction performance for an extended time span compared to existing studies. Therefore, it is anticipated that implementation of this long-term prediction model will offer greater energy savings and improved indoor environmental conditions through optimizing the set-point temperatures.

Accommodating thermal features of commercial building systems to mitigate energy consumption in Florida due to global climate change

Space cooling plays a major role in the commercial building energy consumption due to hot and humid climate in Florida. Although many studies have found that global warming would cause a decrease in heating requirements and an increase in cooling requirements, few of the studies propose mitigation of the cooling demands of the buildings. This study explores the mitigation measures, which include the thermal resistance features of wall and roof systems, visible and solar transmittance values of glazing material, thermal conductivity of windows, and set point temperatures of commercial buildings in Florida. The cooling demands are simulated by using the projected climate weather data in the periods between 2020 to 2100 in different cities. The cooling demands are reduced at various rates between 1% and 5% in all studied building types and in all climate zones by changing the thermal resistances of roofing systems from R-12/14/16 to R-19/21 and wall systems from R-13 to R-19/21. Increasing the wall thermal resistance features is more efficient in mitigating the cooling demands of buildings than increasing the thermal resistance features of roofing systems. In addition, the average values of visible transmittance and solar transmittance of glazing materials and thermal conductivity of windows for a high-rise apartment are suggested to be 0.3, 0.2 and 0.3 respectively.

Benchmarking energy performance for cooling in large commercial buildings

Urban development rapidly increases the total area and the energy consumption of large commercial buildings. Accordingly, the total energy consumption is the focus of numerous energy saving studies. An optimal benchmarking system provides a recommended energy consumption level for each building as well as identifying and prioritizing challenges to energy saving in each building. Based on detailed sub-metering system data and building operational data, this paper presents a simplified energy consumption benchmarking method for air-conditioning systems that cool large commercial buildings. Firstly, a multi-level benchmarking index system is established. Next, the energy performance data of eight large shopping centers in China validates the simplified benchmarking method and finally, the energy performance data of the participating shopping centers is analyzed under the present method. Our data focus on the energy saving potential, as well as informing improvement strategies for building energy performance, that can be used for efficient energy benchmarking process for cooling in large-scale commercial buildings.

Daylighting and energy performance design for single floor commercial hall buildings

PurposeElectric lighting accounts for a large share of energy consumption in commercial buildings. Utilization of daylight can significantly help to reduce the need for artificial lighting, increase workers productivity, customers’ satisfaction and consequently improve sales. However, excessive use of glazing and absence of lighting controls can contribute greatly to higher energy need for heating and cooling and cause undesired glare effects. Thus, optimizing the size, position and materials of external glazing, with the addition of deflectors and dynamic artificial lighting, can become key aspects in the design of sustainable low energy buildings. The purpose of this paper is to analyze daylight potential and energy performance of a hall-type commercial building, situated in the cold climate of Finland, by utilizing different combinations of skylights, windows and lighting controls.Design/methodology/approachThe authors have used computer simulations to estimate daylight and energy performance of a single floor commercial building in relation to various combinations of skylights and windows with variable glazing materials, light deflectors and zonal lighting controls.FindingsThe results show that electric light energy saving potential ranges from a negligible 1.9 percent to a significant 58.6 percent in the case of glass skylights and wall windows using multi-zone lighting control. Total delivered energy ranges between increase of 1.5 and 21.2 percent in the cases with single zone lighting control and between decrease of 4.5 percent and increase of 4.5 percent in the cases with multi-zone control. The highest decrease in primary energy consumption was 2.2 percent for single zone and 17.6 percent for multi-zone lighting control. The research underlines the significant potential of electric light energy savings using daylighting strategies that, including the control of direct solar access for glare and internal gains, can be more than 50 percent.Originality/valueThis research combines accurate daylight and energy assessment for commercial hall buildings based in cold climate region with multiple design variations. The novelty of this work is the consideration of interior elements, shelves and deflectors, in the calculations. This is made possible through the combined use of validated simulation platforms for detailed annual daylighting and electric lighting calculation (Radiance and Daysim) and energy analysis (IDA-ICE, Equa Simulation AB). This method allows to obtain a reliable assessment of the potential of using natural light sources in buildings.

Linking building energy consumption with occupants’ energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)

Occupants’ energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants’ energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants’ energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants’ energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings.

都市域における動的な建築エネルギー需要の予測手法の開発 非住宅建築物の環境関連データベース(DECC)を活用した予測モデルの初期検討

都市域における動的な建築エネルギー需要の予測手法の開発 非住宅建築物の環境関連データベース(DECC)を活用した予測モデルの初期検討

Simulation based mixed mode building design

Air-conditioning represents the major component of energy consumption in commercial buildings in India. A methodology is proposed to reduce the space cooling load by optimizing the building design using incremental integrated design approach and experimental design methods. This methodology is applied over a three story building in New Delhi climate. For the building, passive strategies are able to reduce the cooling load by 50% over the base case and low energy cooling techniques are able to reduce the cooling load by 50% over the passive case using incremental integrated design. Building is then optimized to use natural ventilation to the extent possible, while considering the uncertainty in input parameters, using design of experiments based methods. It is found that during winter months, building can be operated just using natural ventilation during office hours, while maintaining thermal comfort. Natural ventilation is able to reduce the energy consumption by about 15% over the incremental design case. The final achieved mixed mode design consumes 55% less energy, better daylighting, views and uses natural ventilation for 30% more hours as compared to the baseline case.

LEVERAGING ARTIFICIAL NEURAL NETWORKS FOR OPTIMIZING ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS

Optimizing energy consumption in commercial buildings is becoming increasingly essential due to escalating energy costs and the demand for sustainable practices. Artificial Neural Networks (ANNs) have emerged as a powerful tool to address these challenges by offering real-time, intelligent solutions that adapt to usage patterns and environmental conditions. Inspired by the human brain, ANNs consist of layers of artificial neurons connected through weighted links. They are trained using historical and real-time data to recognize complex consumption patterns, predict future energy needs, and efficiently adjust systems like lighting, heating, ventilation, and air conditioning (HVAC). One of the primary advantages of ANNs is their ability to provide highly accurate energy demand forecasts. By analyzing extensive datasets—such as building usage, weather conditions, and occupancy levels—ANNs offer more precise predictions compared to traditional methods. This allows for proactive adjustments to HVAC systems, thereby enhancing energy efficiency and minimizing waste. ANNs also excel in adaptability, continuously learning and adjusting to new information and changing conditions, which is particularly valuable in environments with fluctuating usage and occupancy. Furthermore, integrating ANNs with Building Management Systems (BMS) adds a layer of intelligence and automation, optimizing energy consumption in real-time while considering parameters like occupancy levels and variable energy costs. This leads to substantial reductions in operational costs and energy waste. ANNs also simplify traditional control systems and enhance occupant comfort by maintaining optimal environmental conditions. However, challenges such as the need for high-quality training data, complex integration with existing systems, and data security concerns must be addressed. Despite these challenges, ANNs demonstrate significant potential for improving energy management in commercial buildings, with promising results from various studies indicating their effectiveness in reducing energy consumption and operational costs.

Energy audit and conservation potential analysis of a large comprehensive commercial building

With the rapid development of large-scale public buildings, energy consumption has increased, of which the energy consumption of comprehensive commercial buildings can reach 10~20 times the common building energy consumption, and has great energy saving potential. In this paper, a large comprehensive commercial building in Chengdu is taken as an example to analyze the energy consumption through the actual energy consumption data, viewed from the energy-saving and emission-reduction and static investment payback period point. The results show that the energy saving rate of the building can be achieved by 32.64%, the emission reduction is 6196.52 t CO2 per year, and the investment recovery period is only about 0.90 years, which provides a reference for similar buildings.

Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata

With the growing trove of publicly available building energy data, there are now ample opportunities to apply machine learning methods for prediction of building energy performance. In this study, we test different predictive modeling approaches for estimating Energy Use Intensity (EUI) for US commercial office buildings and the individual energy end-uses of HVAC, plug loads, and lighting, based on the latest Commercial Building Energy Consumption Survey (CBECS) 2012 microdata. After preliminary statistical analysis, six regression or machine learning techniques are applied and compared for prediction performance. Among all candidates, Support Vector Machine and Random Forest demonstrate both accuracy and stability. However, machine learning algorithms are better than the linear regression only to a limited extent, with on average 10–15% lower prediction errors for Total EUI prediction. Conversely, linear regression models slightly outperform machine learning methods in estimating Plug Loads EUI. These mixed results suggest careful consideration in applying advanced predictive algorithms to the CBECS dataset. Individual variable importance was tested using Random Forest, with the top 10 predictors differing for the total and sub-system EUIs. The analysis demonstrates that, for the techniques applied, the variables reported in CBECS have inadequate predictive power to map actual energy consumption. Filling information gaps in areas such as occupant behavior, power management, building thermal performance, and their interactions may help to improve predictive modeling.

Gradient boosting machine for modeling the energy consumption of commercial buildings

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model’s performance. The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

非住宅（民生業務部門）建築物のエネルギー消費量データベース構築に関する研究 大学・研究機関,スポーツ施設のエネルギー消費特性について

非住宅（民生業務部門）建築物のエネルギー消費量データベース構築に関する研究 大学・研究機関,スポーツ施設のエネルギー消費特性について

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

非住宅(民生業務部門)建築物の環境関連データベース構築に関する研究 北信越地域における平成26年度エネルギー消費調査結果

非住宅(民生業務部門)建築物の環境関連データベース構築に関する研究 北信越地域における平成26年度エネルギー消費調査結果

Machine learning approaches for estimating commercial building energy consumption

Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology.

B-15 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 九州地域における平成23年度夏季節電対策の実態調査結果

B-15 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 九州地域における平成23年度夏季節電対策の実態調査結果

B-16 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 効果的な節電対策に関する分析

B-16 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 効果的な節電対策に関する分析

H-58 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 事務所建築物における調査結果(H21年度調査) : (その1)調査建物概要とエネルギー消費

H-58 非住宅(民生業務部門)建築物のエネルギー消費量データベース構築に関する研究 : 事務所建築物における調査結果(H21年度調査) : (その1)調査建物概要とエネルギー消費