Energy Consumption In Office Buildings Research Articles

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyper-parameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi'a is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32 % to 1.38 %, and the coefficient of variation (CV) from 7.5 % to 1.2 %.

Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings

The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings.

Economic optimization of exterior wall insulation in Chinese office buildings by coupling artificial neural network and genetic algorithm

Envelope insulation is an essential measure to reduce building energy consumption and is the focus of many building energy codes. In China, the current standard (GB 55015-2021) only specified limit values for the thermal conductivity (U) of the exterior walls, and it ignored variations in building characteristics and internal parameters. Therefore, it is difficult for architects to quickly determine the exterior wall U-value in building design and retrofit. To address this issue, the exterior wall U-values of office buildings in China were economically optimized by TRNSYS, ANN, and GA. To begin with, the effect of enhancing exterior wall insulation on the heating and cooling energy consumption of office buildings was investigated by TRNSYS software, taking into account variations in building characteristics. Moreover, the exterior wall U-values of office buildings were optimized by coupling ANN and GA. The results showed that the design values of the GB 55015-2021 did not achieve the optimal results in China, and the most economical exterior wall U-value was 0.24 W/(m2·K) in Harbin, 0.24 W/(m2·K) in Beijing, 0.42 W/(m2·K) in Changsha, 1.82 W/(m2·K) in Guangzhou, 0.66 W/(m2·K) in Kunming. Compared with the pre-optimization, the annual life cycle cost of building heating and cooling reduced by 0.96 % in Harbin, 2.07 % in Beijing, 1.44 % in Changsha, 1.68 % in Guangzhou, and 7.37 % in Kunming. In addition, variations in building parameters should be considered in the design phase of new buildings or in the renovation of old buildings, such as internal heat gain, which may lead to different optimal results.

Occupant’s Thermal Perception in Mixed-mode Office Buildings of the Tropical Climate

Thermal comfort and energy consumption in office buildings is a global critical concern. This study investigated this challenge in the Faculty of Environmental Technology, Abubakar Tafawa Balewa University, Bauchi-Nigeria. Through a field survey and physical environment measurements. The study explored occupant perceptions of thermal comfort and satisfaction. It evaluated the thermal comfort and satisfaction of occupants in a mixed-mode office building, focusing on potential discrepancies between perceived comfort and internationally recommended standards. Despite air temperatures falling outside the PMV model's comfort range, high thermal comfort, and satisfaction levels were reported by the respondents. These findings align with other studies in Nigeria, suggesting adaptation and acclimatization to local conditions. The study further examined the relationship between thermal comfort and occupant satisfaction. The result revealed a moderate positive association, suggesting increased thermal comfort leads to higher satisfaction among occupants. While thermal comfort explained 25% of the variance in satisfaction scores. Finally, the study suggests the localization of comfort standards, improved mixed-mode system performance, and encouraging evidence-based design interventions that will ultimately benefit both occupants and the environment.

A Study on the Effect of Dynamic Photovoltaic Shading Devices on Energy Consumption and Daylighting of an Office Building

Photovoltaic shading devices (PVSDs) have the dual function of providing shade and generating electricity, which can reduce building energy consumption and improve indoor daylighting levels. This study adopts a parametric performance design method and establishes a one-click simulation process by using the Grasshopper platform and Ladybugtools. The research focuses on the effect of dynamic PVSDs on daylighting and energy consumption in an office building in Qingdao. The optimal configuration of PVSDs for each month under three dynamic strategies (rotation, sliding, and hybrid) is determined here. Additionally, different control strategies and fixed PVSDs are compared to clarify the impact of various control strategies on daylighting and energy consumption. The findings reveal that, compared to no shading, dynamic PVSDs in the rotation strategy, sliding strategy, and hybrid strategy can achieve energy savings of 32.13%, 47.22%, and 50.38%, respectively. They can also increase the annual average UDI by 1.39%, 2.8%, and 3.1%, respectively. Dynamic PVSDs can significantly reduce the energy consumption of office buildings in Qingdao while improving indoor daylighting levels. A flexible control strategy that adapts to climate change can significantly improve building performance. This research can provide theoretical, methodological, and data support for the application of the PVSD in cold-climate regions in China.

Effects of Cavity Depth of Glass Facades on Energy Performance of the Office Building Facade Scenarios of Dhaka City, Bangladesh

The concern of warming brought on by extreme solar radiation entering the glazed façade may become serious in Bangladesh, where high-rise buildings now commonly use these systems. This scenario will lead to higher energy usage since there will be a greater requirement for electricity to cool the indoor environment. Determining the effects of the Cavity Depth of Glass facades on the energy consumption of office buildings in Dhaka, Bangladesh, is the primary goal of this study. To establish techniques for optimization, different cavity depths, and other glazed façade system parameters have been proposed. Results were obtained by modelling the variables of the investigation using the computer simulation software EnergyPlus. As per the findings of the study, glazed façade systems in Dhaka may benefit greatly from a cavity depth of 1000 mm combined with bronze laminated glass. The glass façade's thermal performance is enhanced under the ideal cavity system since there is a discernible drop in the building's annual end-use energy of about 12.28 % and a drop in indoor temperature of roughly 2°C.

Optimization of exterior wall insulation in office buildings based on wall orientation: Economic, energy and carbon saving potential in China

The aim of this study is to optimize the exterior wall insulation of different orientations to further reduce the heating and cooling energy consumption of office buildings, and to analyze its economic, energy and carbon saving potential in China. A six-story office building was selected as the case building and its energy consumption was evaluated using TRNSYS, considering variations in latitude, window-to-wall ratio, aspect ratio, and exterior wall U-values of four orientations. In addition, the exterior wall insulation in four directions was optimized by coupling artificial neural network and genetic algorithm. The main results showed that in the 20°N to 40°N region of China, the GA-optimized exterior wall U-values were highest in the southern direction, followed by the eastern and western directions, and finally the northern direction. In addition, overall, the optimal combination of exterior wall U-values showed a trend of high at low latitudes and low at high latitudes, and increased with increasing WWR and decreased with increasing AR. Finally, compared to the exterior wall requirements for energy efficient buildings in GB 55015-2021, the optimized office buildings at low altitude in China showed a 3.26 %, 6.77 % and 6.59 % reduction in ALCC, total energy consumption and total carbon emissions respectively.

Analysis of Electrical Energy Consumption in Office Buildings of the Institute Technology of Sumatra in Energy Conservation and Efficiency Efforts

Electrical energy is one of the basic human needs in carrying out various activities or daily activities, especially in office buildings such as office buildings of state higher education institutions, namely the Sumatra Institute of Technology (ITERA). Office buildings are the third highest consumer of electrical energy after shopping centers and hotels. The high demand for electrical energy requires the user sector to make efficiency in energy use. Before making efficiency, it is necessary to know the profile of electrical energy consumption including energy consumption intensity (ECI) and significant energy users. Therefore, in this study, an analysis of electrical energy consumption in each ITERA office building was carried out, through observation and interviews according to SNI / ISO 50002: 2018 energy audit procedures. The results obtained show that the total energy use of the 6 largest buildings (A, B, C, D, E, and F) in May 2023 is 144768 kWh and from the calculation of the ECI value is in the very efficient category (7.44 kWh/m2/month). From 6 existing buildings, there are 3 buildings that are very efficient, 1 building is efficient, and 2 buildings are quite efficient. Then, significant energy users were found by 83% in building C AC. Recommendations for saving energy in the effort of energy conservation and efficiency that can be done include, making changes in habits / patterns of use of electrical energy user equipment, especially in air conditioners and dispensers, so as to save electricity 2-21%, and retrofitting refrigerant AC types of split ducts and cassettes that still use R-22 with MC-22 which can save electricity 15-25%.

Indoor environment quality effects on occupant satisfaction and energy consumption: Empirical evidence from subtropical offices

This study examines the impact of indoor environment quality (IEQ) on occupant satisfaction and energy consumption in Australia’s subtropical office buildings. First, Kano’s model was applied to 201 office occupants' satisfaction surveys to understand the influence of each IEQ parameter on overall occupant satisfaction. Then, a quantitative relationship between monitored IEQ parameters and energy consumption in three office buildings was developed during summer, winter and transitional months. Using Kano’s model, IEQ parameters were classified into Basic Factors, Performance Factors, and Bonus Factors based on their impact on occupants’ overall satisfaction. Basic Factors include ‘acoustic environment’, ‘lighting environment’, ‘level of privacy’, and’office cleanliness’, which were deemed a prerequisite for occupant satisfaction. As Performance Factors, ‘thermal environment’, ‘indoor air quality’, ‘furnishing’, ‘available space’ and ‘personal control’ had proportional relationships with overall satisfaction. The only identified Bonus Factor positively impacting overall occupant satisfaction was ‘View from the windows’. In subtropical climates, the relationship between IEQ parameters and energy consumption reveals that artificial lighting is the most energy-intensive IEQ parameter. The findings also suggest that Australian building experts may have over-emphasised the role that achieving thermal comfort plays in the energy consumption of office buildings in Australia’s temperate climates.

Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm

To gain building energy consumption information during the design phase, the variance analysis to identify significant factors affecting energy consumption in China cold-region office buildings are carried out in this study. Key factors are selected, and prediction models for energy consumption in cold-region office buildings are established using BP and GA-BP algorithms. Three prediction model evaluation indexes are introduced to evaluate the prediction accuracy of the models. The results show that the maximum RMSE of the BP neural network prediction model is 0.498, and the maximum MAPE is 0.797%. Furthermore, the GA algorithm is used to optimize the BP neural network, resulting in a prediction model with a maximum RMSE of 0.359 and a maximum MAPE of 0.289%. The prediction accuracy of the GA-BP algorithm is better than that of the BP algorithm.

Investigation into energy performance of a multi-building complex in a hot and humid climate: efficacy of energy saving measures

PurposeCommercial buildings, which include office buildings, are one of the three major energy-consuming sectors, alongside industrial and transportation sectors. The vast increase in the number of buildings is a positive sign of the rapid development of Malaysia. However, most Malaysian government office buildings tend to consume energy inefficiently due to lack of energy optimization. Most of the previous studies focused on the performance of green buildings in fulfilling the green development guidelines. As such, it is essential to study the energy performance of existing government office buildings that were constructed before most energy-efficient standards were implemented to mitigate energy wastage due to the lack of energy optimization. This study aims to analyse the energy performance of existing non-green Malaysian government office buildings and the factors that influence building energy consumption, as well as to evaluate the efficacy of the existing energy conservation measures.Design/methodology/approachThis study was conducted by a literature review and case study. The chosen buildings are six government office building blocks located in Kuala Lumpur, the capital city of Malaysia. In this study, a literature review has been conducted on the common factors affecting energy consumption in office buildings. The energy consumption data of the buildings were collected to calculate the building energy intensity (BEI). The BEI was compared to the MS1525:2019 and GBI benchmarks to evaluate energy performance. SketchUp software was utilized to illustrate the solar radiation and sun path diagram of the case study buildings. Finally, recommendations were derived for retrofit strategies based on non-design factors and passive design factors.FindingsIn typical government office buildings, the air-conditioning system consumed the most energy at 65.5%, followed by lighting system at 22.6%, and the remaining 11.9% was contributed by office appliances. The energy performance of the case study buildings is considered as satisfactory as the BEI did not exceed the MS1525:2019 benchmark of 200 kWh/m2/year. The E Block recorded the highest BEI of 183.12 kWh/m2/year in 2020 due to its north-east orientation which is exposed to the most solar radiation. Besides, E Block consists of rooms that can accommodate large number of occupants. As such, non-design factors which include higher occupancy rate and higher cooling demand due to high outdoor temperature leads to higher energy consumption. By considering passive design features such as building orientation and building envelope thermal properties, energy consumption can be reduced significantly.Originality/valueThis study provided a comprehensive insight into the energy performance of Malaysian government office buildings, which were constructed before the energy-efficient standards being introduced. By calculating the BEI of six government office buildings, it is found that the energy performance of the case study buildings fulfils the MS1525 benchmark, and that all their BEIs are below 200 kWh/m2/year. Malaysia's hot and humid climate significantly affects a building's cooling load, and it is found the air-conditioning system is the major energy consumer of Malaysian government office buildings. This study discusses the efficacy of the energy-saving measures implemented in the case study buildings to optimize energy consumption. Recommendations were derived based on the non-design factors and passive design factors that affected the energy consumption of the case study building. It is envisioned that this study can provide practical strategies for retrofit interventions to reduce energy consumption in Malaysian office buildings as well as for office buildings that are in a similar climate.

Identifying the most influential parameters in predicting lighting energy consumption in office buildings using data-driven method

Identifying the most influential parameters in predicting lighting energy consumption in office buildings using data-driven method

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

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.

Self-Powered Airflow Sensor Based on Energy Harvesting of Ventilation Air in Buildings

Heating, ventilation, and air conditioning (HVAC) systems account for one-third of the total energy consumption in office buildings. The use of airflow measurements to control the operation of HVAC systems can reduce energy consumption; thus, a sensor capable of monitoring airflow in a duct system is critical. Triboelectric nanogenerators (TENGs) can be utilized as self-powered sensors in airflow-driven TENGs (ATENGs) as self-powered sensors. By employing ferroelectric materials and surface modifications, the surface charges of TENGs can be increased. In this study, fibrous-mat TENGs were prepared using ferroelectric materials consisting of poly (vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) and polyamide 11 (nylon-11). And these materials were subsequently investigated. Poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) was added to PVDF-TrFE to enhance the ferroelectric crystalline phase. X-ray diffraction analysis revealed that this incorporation affects the β phase. In addition, the surface of nylon-11 was modified using the electrospray technique for post-treatment, thereby improving the interfacial adhesion between the fibers. These materials were then utilized in fibrous-mat ATENGs (FM-ATENGs) to demonstrate their practical application. The FM-ATENGs can be effectively used in an Arduino airflow-check sensor, showcasing their potential for application in HVAC systems, to enhance airflow control and energy efficiency.Graphical

External shading and its effect on the energy efficiency of Tehran's office buildings

AbstractModern office building designers tend to propose highly glazed façades that provide occupants with a view and daylight; however, the use of such façades has consequences, like glare and high energy consumption, especially in warm climates. Shadings affect energy usage for cooling, heating, and artificial lighting, defined as the objectives and output in the optimizer algorithm. The input variables are shading type, WWR, the depth of the shading, the distance between the shading and the window, the distance between fins, the number of fins in shading, and the distance from the top of the window. This research aims to comprehensively study external shadings and window‐to‐wall ratios to reduce the high energy consumption in modern office buildings in Tehran and cities in locations with a similar climate. The results can benefit all scientists researching building science, architects, instructors, and those wanting to buy a low‐energy consumption office. Energy consumption was calculated in Ladybug–Honeybee; the best objectives were obtained for each shading with the optimization process by the Octopus plugin in Grasshopper. Results were analyzed and rated compared with a window without shading. The best scenario was obtained for Egg‐crate with a 14.86% reduction in cooling energy and a 20.04% increase in useful daylight illuminance.

Educational Level Impact on Energy Consumption in Malaysia Office Building using Neural Network Method

The level of education has had a considerable impact on energy usage. Office activity requires energy, and this one area is especially subject to changes in demand patterns. The understanding on this trend is applicable to office buildings which has found many benefits like cost saving and better productivity among workers who have access to higher education level. Neural networks are appealing in the field of prediction. The use of artificial neural network technology provides a benefit in reducing the energy consumption in office buildings. The neural network uses neural networks to predict based on capacity. The researchers used a neural network model to predict energy consumption based on data from more than 1,006 samples taken from 13 office building location in 150day periods. The educational level has significantly influence on the energy consumption of an office building. The result indicates that the Degree and above categories contribute higher usage of energy. This study sought to address the contradiction between previous research on educational level impact on energy consumption by examining the levels of educational attainment over time and calculating the impact on building energy consumption.

Characteristics of office lighting energy consumption and its impact on air conditioning energy consumption

The energy consumption of office buildings has increased sharply since the 21st century with the increasing urbanization coverage. At this stage, the energy consumption of office buildings in China mainly comes from air conditioning and lighting. This paper obtains the regression equation of natural lighting illumination and radiant heat gain under different curtain conditions through experiments and simulations, and gives the theoretical calculation process of curtain regulation of office thermal and light environment and energy consumption. DeST and Ecotect software were used to analyze the thermal and light environment and energy consumption of the office. The results show that setting curtains in the room can effectively improve the indoor thermal light environment, the higher the transmittance of curtains the lower the lighting energy consumption, the higher the number of high temperature hours of natural room temperature and the greater the radiant heat gain. The room with thick curtains has the lowest energy consumption for air conditioning in summer, followed by the room with thin curtains, and the room without curtains has the highest energy consumption for air conditioning. The room without curtains has the lowest energy consumption for artificial lighting, the room with thin curtains has the second lowest, and the room with thick curtains has the highest electricity consumption for artificial lighting. The air conditioning energy consumption accounts for the main building energy consumption, followed by the lighting energy consumption. After setting curtains in the office, the air conditioning energy consumption caused by the solar radiation heat entering the room through the exterior windows is smaller than the lighting energy consumption caused by artificial lighting. Therefore, the installation of internal shading with moderate transmittance and good thermal insulation performance can make the energy saving of lighting and air conditioning energy consumption reach the optimal value.

Impact of COVID-19 Pandemic on Energy Consumption in Office Buildings: A Case Study of an Australian University Campus

Building energy management, in terms of both adopted technologies and occupant consumption behaviour, is becoming an essential element of sustainability and climate change mitigation programs. The global COVID-19 pandemic and the consequential lockdowns and remote working had a notable impact on office building operations and provided a unique opportunity for building energy consumption studies. This paper investigates the COVID-19 effects on energy consumption in office buildings, particularly in the education sector. We studied different buildings at the University of Technology Sydney (UTS) campus before and during the pandemic period. The results demonstrate that the changes in energy consumption due to COVID-19 in different UTS faculties are not as strongly correlated with occupant activity. The comparison shows that buildings with administrative offices or classrooms are easier to switch to a remote-working mode than those housing laboratories and special equipment. During weekends, public holidays, or conditions requiring working from home, the per capita energy consumption increases significantly translating into lower energy efficiency. Our findings highlight the essential need for some changes in office building energy management systems. We provide recommendations for office and commercial buildings in general to deal with similar crises and to reduce energy overconsumption in normal situations.

Impacts of Building Microenvironment on Energy Consumption in Office Buildings: Empirical Evidence from the Government Office Buildings in Guangdong Province, China

Social progress and economic development has resulted in the need to focus on the impacts of building microenvironment on the energy consumption in office buildings. The concept of a building’s microenvironment was introduced to understand the local microclimate around a building that is formed by the surrounding urban green spaces, the distribution of roads, and building proximity. For this research, we adopted a regression analysis to quantify the impacts of building microenvironment on energy consumption in office buildings. Taking the government office buildings of Guangdong Province as an example, we measured the building microenvironment through the urban green space density, road density, and number of points of interest (POI) around the buildings. The results showed that when the green space density increased by one unit, the energy consumption in government office buildings was reduced by 0.277%. Moreover, an increase of 1% in road density and in the number of POI increased the energy consumption in government office buildings by 0.288% and 0.048%, respectively. Furthermore, we discussed the heterogeneous impacts of building microenvironment on the energy consumption in government office buildings at varying scale levels. Green space and road density had less impact on the energy consumption in larger buildings, whereas the number of POI had no significant impact on small-scale buildings but did have a significant impact on large-scale buildings. There were also some limitations in the study. The data were limited to government office buildings, and did not include panel data, as well as it lacked building characteristics such as orientation, floor height, and building materials. In addition, it was impossible to evaluate the impacts of meteorological factors such as wind speed and thermal radiation on energy consumption in buildings. Nonetheless, our study demonstrates that energy-aware urban planning and design have the potential to unlock energy efficiency for cities worldwide.