Public Building Energy Consumption Research Articles

Factors Influencing Public Building Energy Consumption: A Case Study of Changjiang River Administration of Navigation Affairs in China

Based on the energy consumption data of the Changjiang River Administration of Navigation Affairs public building located in Wuhan, China, this paper analyzes the energy consumption characteristics of public buildings. Using the DesignBuilder energy simulation software (v7.0.0.096), various factors influencing building energy consumption are investigated. Furthermore, SPSS (v26.0.0.0) is employed to conduct correlation analyses between measured and simulated energy consumption data. The results indicate that the most significant factors affecting energy consumption are outdoor temperature, office equipment usage time, anticipated room temperature settings, boiler usage time, and per capita daily water consumption. By exploring the factors influencing energy consumption in public buildings, this study contributes novel insights into setting targets, adjusting schemes, and achieving energy-saving effects.

Energy consumption baseline for public schools in the West Bank-Palestine

Schools play a significant role in social development which is one of the three major pillars of sustainable development. Schools are also a hub to promote sustainable practices, and hence, it is imperative to measure the actual energy performance of school buildings. This research was designed to quantify the actual energy consumption in public school buildings in the West Bank in Palestine to establish a baseline for energy consumption in public school buildings. Methodologically, the historical energy performance of a representative random sample consisting of 103 schools in the West Bank was statistically analysed. The results show that the average annual energy consumption in public school buildings is 10,368 kWh, while the annual energy use intensity is around 8.35 kWh/m2/year, and 35.44 kWh/student/year. The results presented in this research are not surprisingly low due to the fact that energy consumption in school buildings is heavily determined by heating, ventilation, and air conditioning (HVAC) systems which often do not exist in public school buildings in the West Bank. This research is the first to provide empirical statistical evidence about the actual amount of energy consumed in public school buildings in the West Bank, as well as it is the first to present a baseline energy consumption for public schools. The results presented in this research can be used as an industry benchmark by policy and decision-makers to assess green schools’ performance which, besides reducing the negative impacts on the environment, have the potential to improve health, productivity and, education environment.

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Energy efficiency and energy-saving evaluation methods for public building energy systems under the dual carbon background

The energy consumption characteristics and energy system structure of each type of building are different, so the evaluation methods and indicators need to be customized for different types of buildings, which increases the complexity of the evaluation. Therefore, a method for evaluating the energy efficiency and energy conservation of public building energy systems under the dual carbon background is proposed. Analyze the coupling coefficient data of energy quality, energy and flow, obtain the base of energy efficiency optimization coefficients, and use twin technology to establish a comprehensive energy efficiency model. Introduce matter element analysis and use matter element extension models to evaluate the energy efficiency and energy-saving effects of public building energy systems. The experimental test results show that the research method can accurately evaluate the energy consumption of public buildings and has an effective evaluation effect on different types of buildings.

Study on program methods for energy monitoring of buildings

Energy monitoring is one of the effective measures aimed at reducing and maintaining energy consumption of residential and public buildings at the proper level, taking into account climatic factors. This article provides information about the process of monitoring the energy consumption of a building. A brief analysis of methods for monitoring the energy consumption of different types of buildings has been conducted. In particular, the methods of "Control and Normalization" based on the Energy-Temperature diagram based on the operating mode of the building are considered. In addition to the methods, information is provided on the information system "energy monitoring of buildings" developed on their basis. Also presented the process of implementing energy monitoring using the developed information system. The use of energy monitoring makes it possible to determine overconsumption in a timely manner and due to this to optimize the current level, as well as to determine the target values of energy consumption in buildings.

Retracted: Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

Retracted: Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

Analysis of the Energy Consumption of Old Public Buildings in South Korea after Green Remodeling

In Korea, green remodeling of public buildings will be mandatory from 2025, and about 30,000 old public buildings nationwide will conduct green remodeling every year. Therefore, in this study, green remodeling was promoted to improve energy efficiency for existing public buildings located in Siheung, Gyeonggi-do. A remodeling method was developed to reduce the cooling and heating load of the building and reduce construction costs. The development technology was applied to the demonstration building to improve the insulation and airtightness performance of the building. In order to analyze the effect of improving the energy performance of the building, the simulation results and energy consumption before and after remodeling were analyzed. The demand for heating energy decreased by about 68.95%, and the demand for cooling energy decreased by 35.21%. According to an analysis of actual energy consumption, the average monthly energy consumption savings from January to September was 17.4%. It was confirmed that the savings were the highest at 35.4% in February, during winter. Applying the remodeling method developed in this study is expected to contribute to reducing greenhouse gas emissions by reducing energy efficiency and energy consumption of existing buildings.

A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings

Intelligent applications have become essential for effectively managing energy consumption in public buildings. Energy management, especially for critical infrastructure buildings and public buildings, often serves as an example of sustainable practices since it tries to increase energy resilience through backup power systems. These structures were dealing with more difficulties, such as tight budgets, complicated regulations, behavioral issues, and others. Managing energy consumption in buildings is a multifaceted undertaking that entails grappling with non-uniform energy usage and a lack of established design guidelines for implementing energy-efficient and sustainable solutions. Consequently, analyzing energy utilization trends in public buildings and projecting future energy requirements is of paramount importance. This comprehension is indispensable to the identification and acknowledgment of energy consumption patterns in commercial and institutional buildings. Our research is highly significant for optimizing energy usage, reducing environmental impact, and making informed decisions in building management. We emphasize the importance of our predictive capabilities, sustainability, and life cycle assessment (LCA) techniques in achieving these objectives. Our hybrid model improves energy efficiency while also making a significant contribution to the larger objective of meeting sustainability standards in the built environment through thorough analyses of real-world data. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. In order to determine the number of energy consumption pattern clusters, a Self-Organizing Map (SOM) model was utilized in conjunction with Principal Component Analysis (PCA). The determination of clustering levels for each structure was executed by utilizing K-means in conjunction with a Genetic algorithm (GA). This approach enabled the identification of optimal clustering levels for the given structures, thereby facilitating effective analysis and interpretation of the data. The GA was specifically employed to determine the optimal centroid points for each cluster, thus optimizing the performance of the fitting model. Cluster analysis pattern extraction has made determining which buildings consume the most energy easier. As intelligent models for predicting energy usage, Convolutional Neural Networks (CNNs) and CNNs paired with a GA have also been used. The application of a GA to modify multiple settings of a CNN was conducted at this stage. The CNN model using a GA shows the method that was used provides higher accuracy and standard error compared to the conventional approach. It achieves a 94.01% accuracy on the training dataset and a 93.74% accuracy on the validation dataset with an error rate of 0.24 and 0.26 respectively. On the training dataset, the accuracy is 89.03% with a standard error of 0.3, whereas on the validation dataset, the accuracy is 88.91% with a 0.33 standard error. This research can help policymakers in the energy sector make better decisions regarding the timing of energy supply and demand for public buildings.

Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.

Problems of Building Energy Consumption in Rural Schools in Linzhi, Tibet

The energy-saving research of school buildings in villages and towns in the severe cold areas of Linzhi Township has not yet been widely carried out. The energy consumption of schools in villages and towns in the severe cold areas of Linzhi is generally high, and the heating energy consumption in winter accounts for a large proportion of the energy consumption of school buildings. With the promulgation and implementation of energy-saving design standards for public buildings in schools in large and medium-sized cities, energy-saving activities have been carried out. In the severe cold area of Linzhi, there are a large number of buildings in villages and towns, with primary schools in each village, secondary schools and central primary schools in each township. The total energy consumption of village and town school buildings accounts for the majority of the total energy consumption of village and town public buildings. From this, it can be seen that studying the characteristics of village and town school buildings in the severe cold area of Linzhi and researching energy-saving design strategies suitable for their own characteristics based on the characteristics of village and town school buildings in the severe cold area are of great significance for the promotion and application of clean energy in schools in the Linzhi area.

HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model

Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper proposes a load forecasting model CEEMDAN-Conv1D-BiLSTM-AM which combines empirical mode decomposition and neural networks. The load data are decomposed into fifteen sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The neural network inputs consist of the decomposition results and five exogenous variables. The neural networks contain a one-dimensional convolutional layer, a BiLSTM layer, and an attention mechanism layer. The Conv1D is employed to extract deep features from each input variable, while BiLSTM and the attention mechanism layer are used to learn the characteristics of the load time series. The five exogenous variables are selected based on the correlation analysis between external factors and load series, and the number of input steps for the model is determined through autocorrelation analysis of the load series. The performance of CEEMDAN-Conv1D-BiLSTM-AM is compared with that of five other models and the results show that the proposed model has a higher prediction accuracy than other models.

Monitoring energy consumption of vending machines in university buildings

Vending machines are a convenient source of food and drinks, but they can also be a significant source of energy consumption in public buildings. To reduce energy waste and improve energy efficiency, a low-cost sensor network was used to monitor the energy usage of vending machines. The sensor network consists of small, wireless sensors that are placed inside the vending machines, and a central hub that is connected to the internet via Wi-Fi. The sensors are able to collect data on the energy usage of the vending machines, including when they are idle, in use, or in a standby mode. This data is then transmitted to the central hub, where it is processed and made available to users through a web-based interface or mobile app. With this information, users can identify patterns in energy usage and make changes to reduce overall energy consumption. In a university in southern Spain, 53 vending machines of three types were monitored: snack, cold drinks, and hot drinks. The average monthly consumptions of these types of vending machines were snack (250 kWh), cold drinks (200 kWh), and hot drinks (100 kWh). By using a sensor network to monitor the energy usage of vending machines, it is possible to identify areas where energy is being wasted and make adjustments to reduce this waste. This can help to reduce the environmental impact of the vending machines, by reducing the amount of energy that is consumed and the associated greenhouse gas emissions. Additionally, by making the vending machines more energy efficient, it is possible to improve their overall productivity and cost-effectiveness. Overall, the use of a low-cost sensor network to monitor vending machines energy usage can be seen as a form of cleaner production, because it promotes the use of more sustainable and efficient industrial processes.

A comprehensive evaluation framework of energy and resources consumption of public buildings: Case study, People's Bank of China

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited.

A Review of Energy Efficiency Interventions in Public Buildings

This research provides a comprehensive exploration of energy efficiency dynamics in non-residential public buildings such as schools, swimming pools, hospitals, and museums. Recognizing the distinct energy consumption patterns of each building type, the study accentuates the unique challenges they present, with a particular focus on the continuous and intensive energy demands of hospitals and the unparalleled energy needs of swimming pools. Through an extensive review of various case studies, the research unveils prevailing energy consumption trends, highlighting the role of metrics in assessing energy efficiency and the inherent challenges these metrics face in ensuring uniformity and direct comparability. A core element of this analysis emphasizes the dual nature of technical retrofitting, categorizing interventions into passive and active measures. The research delves into the sustainability imperatives of energy interventions, exploring the economic motivations underpinning retrofit decisions, and the intricate relationship between advanced technological solutions and the behavioral tendencies of building operators and users. Additionally, the study uncovers the influence of external determinants such as climatic factors and government policies in shaping energy consumption in public buildings. In synthesizing these findings, the paper offers insightful recommendations, emphasizing the need for an integrated approach that harmonizes technological innovations with informed operational habits, aiming to optimize energy efficiency in public non-residential buildings.

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Gaussian Mixture Model based pattern recognition for understanding the long-term impact of COVID-19 on energy consumption of public buildings

Analysis of the energy performance strategies in a historical building used as a music school

Energy consumption in public education buildings depends on use and occupancy. To improve energy performance, energy audits are essential to identify specific solutions for each building. In this study, we conducted an energy audit of a historical building used as a public education centre. We collected gas and electricity bills and recorded indoor temperatures for over a year to determine heating and cooling set points and the schedules. Our analysis showed that 47.42% of the electricity bills were unused. To reduce energy demand and improve thermal comfort, we both developed and validated a Building Energy Modelling (BEM) approach using TRNSYS18 and weather data during year 2021. The BEM model allowed us to propose efficient measures to meet the Standard Passive requirements. Our results demonstrate the effectiveness of energy audits and BEM modelling in reducing energy consumption in public education buildings.

Incentive Mechanism and Subsidy Design for Continuous Monitoring of Energy Consumption in Public Buildings (CMECPB): An Overview Based on Evolutionary Game Theory

Rapid urbanization and the continued expansion of buildings have resulted in a consistent rise in the energy consumption of buildings. At the same time, the monitoring of building energy consumption has to achieve the goals of an “Emission peak” and “Carbon neutrality”. Numerous energy consumption monitoring systems have been established in several types of public buildings. However, there is a need to ensure that the data are continuously acquired and of superior quality. Scholars have noted that the in-depth research connected to the continuous monitoring of energy consumption in public buildings (CMECPB) is currently sparse. As a result, additional precise quantitative studies targeting the behavior of various stakeholders are also lacking. Hence, there is a need to explore the definition of value and the dynamic benefits of relevant subjects in continuous energy consumption monitoring based on evolutionary game theory and to propose incentive policies. This paper constructs an evolutionary game model for CMECPB between an energy service company (ESCO) and its owner to study the dynamic evolution path of a game system and the evolutionarily stable strategy under market-based mechanisms. Furthermore, by introducing government actions, the incentive policies and subsidy strategy for different subjects of interest are probed in detail by developing a principal-agent model to explore the incentive strength. The following conclusions can be reached: (1) it is inefficient and risky to rely only on the owner and the ESCO in achieving the optimal Pareto equilibrium; (2) the optimal incentives are “fixed incentives” in the case of information symmetry and a “fixed incentive + variable incentive” in the case of information asymmetry; (3) the choice of optimal incentive strategy is also influenced by the cost effort coefficient, risk aversion, external uncertainty, and integrated value transformation coefficient; (4) the incentive intensity and subsidy should be determined by comprehensive analysis with multiple indicators based on the conventional value of a project and the external value of a particular project. An in-depth understanding of each component of the CMECPB pathway yields insights into overcoming the challenges of building energy saving. Furthermore, the results may be useful in developing targeted, effective incentive policies for different disciplines and promoting the continued progress of monitoring building energy consumption and building energy efficiency.

A novel method on the optimization problem of energy conservation in public buildings

ABSTRACT In recent years, the problem of energy consumption has received considerable attention. Among them, the proportion of energy consumption in public buildings accounts for a substantial part, and there is a large space for energy saving in this field. Practically, the major limiting factor for energy consumption optimization is indoor environment comfort degree. As such, a trade-off problem between energy conservation and indoor environment must be resolved. In this paper, an office in a public building is chosen as the experimental object, with 2520 real observations collected for two months, a series of intelligence based techniques are combined and applied to deal with this optimization problem. First, Partial Rank Correlation Coefficient (PRCC) was employed to perform correlation analysis for a set of candidate variables, such that the computational time of subsequent model training is reduced by 8.32 seconds as compared to the case where no PRCC is in use. Following this, Genetic Algorithm (GA)-based Levenberg-Marquardt algorithm (LM) are applied collectively to optimize the conventional Back Propagation (BP) neural network, and results suggest that there was a marked improvement in neural model performance, on one hand the epoch number for convergence drops from 72 to 13, on the other hand the mean squared error for test set is reduced by 7.46e-4 (from 9.93e-4 to 2.47e-4). Finally, pigeon-inspired Optimization (PIO) algorithm is employed to address the formulated optimization problem and the optimal value for Predicted Mean Vote (PMV) and Indoor Air Quality (IAQ) are −0.4775 and 0.007826, respectively. To verify the validity of our results that derived by the proposed approach, corresponding parameters are applied in a practical scenario. The experimental result indicates that the energy efficiency is improved by around 18.6% compared to that yielded before optimization is performed, which demonstrates the applicability and effectiveness of the proposed method.

Low-carbon progressive design for sustainable development of buildings

With the advance of urbanization, energy consumption of public buildings is increasing rapidly, and it is urgent to achieve sustainable development of buildings. For public buildings, its energy-efficient envelope design is of great importance and necessity, which has been widely adopted to reduce energy consumption and CO2 emissions without sacrificing indoor thermal comfort. In this study, a progressive design approach of building envelope is proposed for energy-saving and low carbon emission. Then, the proposed method was applied in an archive building in Nanjing, China, with multiple rounds of design modifications of building envelopes through numerical simulations of building performance. The indoor temperature fluctuation, building energy consumption and CO2 emission were evaluated. Compared to the initial design, the optimized design can save 20.00%, 25.66% and 20.97% of building energy consumption in summer, winter and the entire year, respectively. Furthermore, the optimized design can reduce 20% and 25% of carbon emission in summer and winter, respectively. The progressive design method for building envelopes can significantly save energy consumption and reduce carbon emissions. In addition, it provides a reference for future green building design and application, which promotes the sustainable development of green and low-carbon buildings.

Stepwise Optimization Method of Group Control Strategy Applied to Chiller Room in Cooling Season

Central air-conditioning systems account for the largest share of energy consumption in public buildings, wherein the chiller room is the main source. The current empirical strategies of chiller room group control have difficulty realizing integrity, timeliness, and equipment adjustment accuracy and lead to energy wastage. Therefore, the operation strategy optimization model is of great value for achieving energy savings. This study proposed a stepwise optimization method for central air-conditioning systems, which is divided into the chilled side (the chiller and chilled water pump) and the cooling side (the chiller, cooling water pump, and cooling tower). The optimal points of the two sides are calculated sequentially and integrated as the global optimization result. To construct the stepwise optimization model, mathematical power models of the equipment in use were established to provide mathematical support for objective functions, and a short-term load prediction model with a matching optimization step size was established for energy constraints. The TRNSYS simulation model was developed to verify the energy-saving effects of the stepwise optimization model according to the energy-saving rates of 6.41% and 13.56% attained in both cases. The stepwise optimization strategy can more effectively guide practical applications and provide another way of thinking with respect to the group control optimization of chiller rooms in public buildings.