Large Public Buildings Research Articles

Electricity Consumption and Efficiency Measures in Public Buildings: A Comprehensive Review

Industrialization and the expansion of service sectors have led to a significant increase in electricity consumption. This rising demand has also been observed in public buildings, which account for a considerable share of total electrical energy use. Coupled with the upward trend in energy prices, this increase has likewise escalated electricity costs in these sectors. The objective of this review is to compile studies that analyze electricity consumption in large public buildings, with a primary focus on universities, as well as works that propose or implement energy-saving measures aimed at reducing consumption. Throughout this review, it is observed that effective monitoring of consumption as well as the use of demand management systems can reduce electricity consumption by up to 15%. Additionally, the studies collected consistently highlight the need for improvements in real-time data monitoring to enhance energy management. Buildings that implement energy-saving measures achieve reductions in demand exceeding 10%, while those incorporating renewable energy systems are capable of covering between 40% and 50% of their energy needs. Of these systems, solar photovoltaic technology is that most widely adopted by public buildings, primarily due to its adaptability to the architectural characteristics and operational requirements of such facilities. This review underscores the substantial impact that optimized monitoring and renewable energy integration can have on reducing the energy footprint of large public facilities.

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building’s refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

Bim-based Digital Twin development for university Campus management. Case study ETSICCP

Innovation and digitalization are outstanding topics acquiring each day more importance for local governments, especially in Facility Management sector. Moreover, during the COVID-19 situation, new management needs emerged, especially in large public buildings. Building Information Modeling (BIM) is considered as one of the emerging technologies used to reach a total digitalization of the infrastructure. Nevertheless, BIM implementation carries important barriers with itself like, high software and hardware investments, initial BIM skills training or low data interoperability. The objective of this project is to overpass those implementation barriers. For this purpose, the paper shows the creation of a BIM-based intelligent platform for infrastructure management that leads to the development of a Digital Twin (DT). To show the potential of the software developed, a real implementation in the Civil Engineering School at Universidad Politécnica de Madrid was carried out, obtaining significant results thanks to the actual feedback of infrastructure users and managers. The novelty of this project relies on the final results achieved, obtaining a complete DT for management functionalities like space reservation, live sensors data or assets management. All of it, linking BIM models with own software and hardware development using Internet of Things and cloud computing. A multidisciplinary work is compiled in this paper, providing the reader with the most relevant challenges detected in a real digitalization process.

Analysis of Multi-Dimensional Layers in Historic Districts Based on Theory of the Historic Urban Landscape: Taking Shenyang Fangcheng as an Example

The accelerated process of urbanisation in China is resulting in a decline in and threat to the historic landscape of historic districts. This study is based on the theory of historic urban landscapes and employs a multi-dimensional layers research framework for historic districts. It adopts a single case study and a research method that combines quantitative and qualitative methods. The stratification elements of Fangcheng in Shenyang are identified and summarised, the process of stratification is analysed, and the stratification patterns and laws are summarised through the acquisition and collection of multivariate data. The findings of this study indicate that the stratification elements of the Fangcheng Historic District have undergone five distinct phases of stratification evolution. The resulting stratification pattern can be summarised as follows: newborn, preserve, override, juxtaposition and decession. The spatial elements are layered in the following pattern: The historic landscape can be conceptualised as comprising four layers: (1) the layering of the historic landscape with large public buildings as the anchor point; (2) the layering of the historic landscape with the spatial pattern as the skeleton; (3) the layering of the historic landscape with the iconic buildings as the nodes; and (4) the layering of the historic landscape with the correlative elements as the substrate. The law of value element layering primarily reflects the principles of concentration, diversity and adaptation. In light of the urban historic landscape theory, the reconstruction and restoration strategies, integration of old and new and adaptive conservation of historic landscapes are proposed to offer novel insights and guidance for the conservation of the historic landscape in the Fangcheng Historic District.

Grid Optimization of Free-Form Spatial Structures Considering the Mechanical Properties

In recent years, the application of free-form surface spatial grid structures in large public buildings has become increasingly common. The layouts of grids are important factors that affect both the mechanical performance and aesthetic appeal of such structures. To achieve a triangular grid with good mechanical performance and uniformity on free-form surfaces, this study proposes a new method called the “strain energy gradient optimization method”. The grid topology is optimized to maximize the overall stiffness, by analyzing the sensitivity of nodal coordinates to the overall strain energy. The results indicate that the overall strain energy of the optimized grid has decreased, indicating an improvement in the structural stiffness. Specifically, compared to the initial grid, the optimized grid has a 30% decrease in strain energy and a 43.3% decrease in maximum nodal displacement. To optimize the smoothness of the grid, the study further applies the Laplacian grid smoothing method. Compared to the mechanically adjusted grid, the structural mechanical performance does not significantly change after smoothing, while the geometric indicators are noticeably improved, with smoother lines and regular shapes. On the other hand, compared to the initial grid, the smoothed grid has a 21.4% decrease in strain energy and a 28.3% decrease in maximum nodal displacement.

Carbon emission measurement and evaluation of large public buildings in the materialization stage

The greenhouse gas emission during its materialization stage is characterized by time-concentrated and large emissions. This study first divided the materialization stage into three segments according to the life cycle theory: building materials production, building materials transportation, and construction. A carbon emission measurement model for the materialization stage of large public buildings was established by integrating the carbon emission coefficient method, and the emission reduction rate (ERR) was proposed as an indicator for quantitatively assessing carbon emissions during the materialization stage of large public buildings. After the initial indicator system was framed, the Analytic Network Process and Fuzzy Comprehensive Evaluation methods were used to establish the evaluation model for large public buildings at the materialization stage. Last, the model was verified at a convention and exhibition center in Xiamen, China, and it shows that the production stage of building materials has a large potential for emission reduction.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Evaluating rooftop rainwater harvesting potential to satisfy urban water demand in Hosaena City, South Central Ethiopia

ABSTRACT The assessment of the rainwater harvesting (RWH) potential in Hosaena City from medium to large public buildings is the main objective of the study. ArcGIS with Google Earth Pro was used to calculate the rooftop area of the selected building, and RWH from each category of public buildings was evaluated by considering rooftop areas, monthly rainfall, and runoff coefficient. The result shows that the highest amount of rainwater was harvested from the City Administration building in August (197.1 m3), whereas the lowest was collected from the Health Center building in December (1.8 m3). Additionally, the contribution of RWH from individual public buildings for each month was evaluated, and its values ranged from 0.073 to 1.8%. The comparison shows that Wachemo University, Nigist Eleni Comprehensive Hospital, a technical and vocational institute, and teacher training center buildings, could have a significant potential for rainwater storage, ranging from 1.56 to 28.41%. This implies that, besides saving a noteworthy volume of potable water, the excess rainwater can be stored and utilized for the later dry months. As a result, it is recommended that rooftop RWH in Hosaena be the best source of the city's water supply system to reduce the current water supply shortage.

Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

In the context of global warming, the focus on applying and researching double–skin facade (DSF) systems to reduce energy consumption in buildings has significantly increased. However, researchers have not thoroughly examined the performance and applicability of DSFs in severe cold regions with high winter heating demands. This study aims to evaluate the potential application of DSFs in the harsh cold cities of Northwest China and investigate their role in enhancing energy efficiency in large public buildings. Through energy consumption simulation and a comprehensive evaluation using the TOPSIS entropy weight method, the effects of applying 20 DSF schemes in four cold cities in Xinjiang (Kashgar, Urumqi, Altay, and Turpan) were analyzed. The experimental results indicate that the average EUI energy–saving rates in Kashgar, Urumqi, Altay, and Turpan are 64.75%, 63.19%, 56.70%, and 49.41%, respectively. South–facing orientation is deemed optimal for DSF in Xinjiang cities, with the highest energy–saving rate reaching 15.19%. In Kashgar, the energy–saving benefits of west–facing DSF surpass those of north–facing DSF. Conversely, the order of orientation benefits for other cities is south, north, west, and east. An analysis of heating, cooling, and lighting energy consumption reveals that Box Windows exhibit superior heating energy efficiency, while Corridors are more effective for cooling. This characteristic is also evident in the optimal installation orientation of various types of curtain walls. Given the relatively higher demand for heating compared to cooling in urban areas, Box Windows yields significant benefits when facing south, west, or north; conversely, if there is a high demand for urban cooling, Corridors should be considered in these three directions. Multistorey DSF systems are suitable for east–facing buildings in Xinjiang cities. Selecting suitable DSF schemes based on specific conditions and requirements can reduce building energy consumption. The research findings offer theoretical guidance for designing and implementing DSF in diverse cities in cold regions.

Indoor navigation map design based on spatial complexity

ABSTRACT In the face of the complex interior structure of a building, indoor navigation is an important tool for people to arrive at their destination in large public buildings. In this research, we first analyze the complexity of indoor space, which mainly includes spatial entities with rich semantics, but corridors lacking in semantics, poor visibility, and multi-dimensional floors. Then, we designed various indoor navigation maps based on spatial complexity and conducted a comparative analysis between the existing maps and the ones we designed. Participants were recruited to perform three navigation tasks in the study area on-site. The results demonstrate the advantages of using a hierarchical landmark representation for improved spatial knowledge acquisition and the effectiveness of scene-enhanced directional guidance in areas with poor visibility. It is worth noting that the introduction of three-dimensional maps was found to have a positive impact on users’ spatial perception and their ability to sketch routes accurately. In summary, this evidence confirms that maps based on spatial complexity not only enhance navigation efficiency but also have the potential to improve the overall indoor navigation experience.

Strength design of built-up radially battened columns subject to axial compression and arbitrary directional bending moment

Built-up radially battened columns (RBCs), comprising several identical circular steel tubes interconnected by multiple radial-shaped battens, represent an innovative column design that combines aesthetic appeal with exceptional load-bearing capacity. This makes them well-suited for architectural applications in large public buildings such as stations and airports. The axial compressive strength design methods for the built-up RBC have been extensively explored in the authors' prior studies. However, in real-world engineering applications, built-up RBCs may experience simultaneous axial compression and varying directional bending moments, particularly when considering potential eccentric compression or wind and earthquake loads in public buildings. Currently, strength design methods for this loading condition are still lacking. To address this deficiency, this paper primarily delves into the failure mechanism and strength design methods for built-up RBCs experiencing combined axial compression and arbitrary directional bending moments. Initially, the sectional strength of the built-up RBC is investigated by using a shell finite element model (FEM) for the built-up RBC. It is revealed that the compression-bending sectional strength of the built-up RBC exhibits significant differences with varying bending directions and tube numbers. Moreover, in some bending directions, the M-N sectional strength curve of the built-up RBC exhibits convexity. By integrating theoretically derived equations grounded in the principles of materials mechanics with FEM numerical calculations, concise and practical design equations are proposed for accurately predicting the sectional strength of the built-up RBC under combined axial compression and arbitrary directional bending moments. Subsequently, The buckling strength of the built-up RBC is studied using the FEM, considering initial geometrical imperfections consistent with first-order flexural buckling and accounting for geometric nonlinearity. It is noted that the bending direction of the built-up RBC may deviate during loading. Besides, the M-N buckling strength curve of the built-up RBC is not as convex as its M-N sectional strength curve. Based on calculations from numerous examples, practical design equations for predicting the buckling strength for the built-up RBC under axial compression and any directional bending moments are introduced. The key findings of this paper contribute to the compression-bending strength design of RBCs, enriching RBC design theory and promoting the widespread application of built-up RBCs in actual engineering projects.

Construction Technology of Steel Structures in Large Public Buildings

With the continuous development of the Chinese economy and the rapid development of large public buildings, the application of steel structures in large buildings is gradually increasing. The use of steel structures is crucial for meeting the needs of large-span, super high-rise buildings, rapid construction, and achieving sustainable development of buildings. Therefore, studying and analyzing the construction technology of large-scale building steel structures has important practical significance. This study is based on a library construction project, focusing on the installation of embedded bolts, steel columns, steel beams, and high-strength bolts in the construction of large building steel structures, as well as the technical schemes and construction processes of welding, anti-corrosion coatings, and fireproof coatings. I hope to provide reference and suggestions for actual construction.

Emergency evacuation based on long range communication technology

The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the Technical Standard for Fire Emergency Lighting and Evacuate Indicating System.

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

Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities.

A Wind Protection Design Strategy for Airport Terminal Door Bucket Space Based on Wind Environment Simulations

Airport terminal buildings are large public transportation buildings with complex functions and dense crowds, and the wind environment has significant effects on passenger comfort and the energy consumption of air conditioning systems. In this study, we investigated Xianyang T3 Terminal as an example and used the Phoenics software to conduct comprehensive simulations based on different door bucket forms and parameters, as well as comprehensively considering the impacts of different outdoor wind environments. A terminal door bucket windproof design strategy was proposed based on our results. The results showed that door buckets could effectively reduce the entry of outdoor wind, and built-in door buckets performed the best. The width of the door bucket should be set to 9.3–11.3 m and the depth of the door bucket to 6.7–7.7 m. The height of the door bucket had little impact. A side door should be added to the door bucket, so it can be opened when the outdoor wind speed is high, and a mechanical ventilation system should be introduced to improve the indoor ventilation.

Indoor thermal nonuniformity of atrium-centered public building: Monitoring and diagnosis for energy saving

Heating ventilation and air conditioning system accounts for over one third of building energy usage, especially for public buildings due to large indoor heat source and high ventilation and thermal comfort requirement compared to residential buildings. Natural ventilation shows high application potentials in public buildings because of its high-efficient ventilation effect and energy saving potential for indoor heat dissipation. In this paper, the building design is conducted for a science museum and library with atrium-centered natural ventilation consideration. The floor layout, building orientation and internal structure are optimized to make full use of natural ventilation for space cooling under local climatic conditions. A natural ventilation model is established through building field tests to evaluate the air flow and thermal environment under indoor and outdoor pressure differences. The preliminary results show that Building A's courtyard exhibited overheating issues, likely attributed to outdoor solar radiation, whereas Building B's courtyard experienced localized cooling, possibly due to indoor air conditioning system controls. Addressing these concerns necessitates modifications in courtyard design and structure. Moreover, both courtyards displayed vertical temperature gradients, emphasizing the need for effective management of outdoor heat influx and enhancements in indoor ventilation and shading strategies. To mitigate these issues, the study proposed three distinct roof design models and more refined indoor air conditioning system control strategies. The optimization of architectural design can achieve a maximum energy-saving rate of 46.54 %. Furthermore, aligning functional zones with thermal comfort areas was recommended to enhance overall building thermal comfort. The findings and proposed solutions from this study are anticipated to contribute to the enhancement of thermal comfort, energy efficiency, and vertical temperature distribution in large public buildings, catering to user requirements while reducing energy consumption. This research holds significant implications for advancing sustainability and environmental preservation in the realm of architecture.

Economic powers encompass the largest cultural buildings: market, culture and equality in Stockholm, Sweden (1918–2023)

PurposeThe aim of this research is to understand the relationship between cultural buildings, economic powers and social justice and equality in architecture and how this relationship has evolved over the last hundred years. This research seeks to identify architectural and urban elements that enhance social justice and equality to inform architectural and urban designs and public policies.Design/methodology/approachThe author explores the relationship between case studies of museums, cultural centers and libraries, and economic powers between 1920 and 2020 in Stockholm, Sweden. The author conducts a historical analysis and combines it with statistical and geographically referenced information in a Geographic Information System, archival data and in situ observations of selected buildings in the city. The author leverages the median income of household data from Statistics Sweden, with the geographical location of main public buildings and the headquarters of main companies operating in Sweden.FindingsThis analysis presents a gradual commercialization of cultural buildings in terms of location, inner layout and management, and the parallel filtering and transforming of the role of users. The author assesses how these cultural buildings gradually conformed to a system in the city and engaged with the market from a more local and national level to global networks. Findings show a cluster of large public buildings in the center of Stockholm, the largest global companies' headquarters and high-income median households. Results show that large shares of the low-income population now live far away from these buildings and the increasing commercialization of cultural space and inequalities.Originality/valueThis research provides a novel image of urban inequalities in Stockholm focusing on cultural buildings and their relationship with economic powers over the last hundred years. Cultural buildings could be a tool to support equality and stronger democracy beyond their primary use. Public cultural buildings offer a compromise between generating revenue for the private sector while catering to the needs and interests of large numbers of people. Therefore, policymakers should consider emphasizing the construction of more engaging public cultural buildings in more distributed locations.

Research on Neural Network Prediction Model of Whole Process Risk Management Based on Building Information Model

With the rapid development of China’s construction industry and the acceleration of urbanization, large‐scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back propagation (BP) neural network and building information model (BIM) technology, this paper carries out the research on risk management process of the whole life cycle of large public buildings and identifies the risk factors of large public buildings from the application dimension and the management dimension. The risk management evaluation index system is constructed and identified, and assessment, early warning, prevention, and control of risk management are applied and analyzed throughout the process. The international large public sports center project is used as a case study to establish a BIM model, while the BP neural network risk management model is used for prediction and calculation. The results of this study show that, first, the maximum deviation rate of the output indicators of the BP neural network risk model is 3.57% in the design period (B2) and the minimum deviation rate is 0.00% in the commissioning period (B4), which verifies the reliability of the training results of the model. Second, the best effect of risk management in the whole life cycle of the building is in the investment period (B1) and the highest risk is in the construction period (B3). Last, this paper constructs a new risk management framework to realise the risk management of the whole cycle of construction projects from design to operation, which helps to improve the management level and risk response ability of construction projects and ensure the smooth and sustainable development of the whole life cycle of construction.

IoT Framework for Early Fire Detection and Control in Public Large Buildings: A Review

Fire destruction in large public and private buildings, particularly in markets, public offices, education, and industries, has devastated properties, costing millions while taking people’s lives. Fire destruction also extends to environmental degradation. Complete fire prevention is unattainable. However, an effort could be made to early fire detection and control to lessen the tragedy. There are several technologies for real-time fire detection, like heat detection, gas detection, flame detection, and smoke detection. A maximal mix of fire-detecting algorithms should be utilized to maximize accuracy and eliminate false fire confirmation. This research paper presents an early fire detection and control framework for large public and private buildings that utilizes the available fire detection technologies reviewed from various pieces of literature. In justification of the framework adaptability, the research paper examined the current state of firefighting infrastructures in large and private buildings. The review was based on ten (10) large public and private buildings. The buildings were divided into four strata (business center's, public offices, education and industries). In addition, information from the Fire and Rescue Force (Mbeya Office) was used to further envision the possibility of the framework adaptation. Generally, effort should be made to ensure old and new large public and private buildings have well-functioning firefighting infrastructures as stipulated in policies and laws for public safety. Furthermore, slight infrastructure improvement is required, particularly in sustainable power supply, internet connectivity, and automatic fire detection and alarm systems, to adapt the presented early fire detection and control framework smoothly.

Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM

Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the genetic algorithm (GA), and a GA-SVM prediction model was proposed to perform building hourly cold load prediction. Then, when there is a demand for the fluctuation prediction of BCL or extreme weather conditions are encountered, the information granulation (IG) method is used to fuzzy granulate the data. At the same time, the fluctuation range of the BCL was obtained by combining the prediction of the established GA-SVM model. Finally, the model was validated with the actual operational data of a large public building in Xi’an. The results show that the CV-RMSE and MAPE of the GA-SVM model are reduced by 58.85% and 68.04%, respectively, compared with the SVM for the time-by-time BCL prediction, indicating that the optimization of the SVM by using the GA can effectively reduce the error of the prediction model. Compared with the other three widely used prediction models, the R2 of the GA-SVM model is improved by 4.75~6.35%, the MAPE is reduced by 68.00~72.76%, and the CV-RMSE is reduced by 59.69~64.97%. This proved that the GA-SVM has higher prediction accuracy. In addition, the joint model was used for BCL fluctuation range prediction, and the R2 of the prediction model was 97.27~99.68%, the MAPE was 2.59~2.84%, and the CV-RMSE was only 0.0249~0.0319, which demonstrated the effectiveness of the joint prediction model. The results of the study have important guiding significance for building load interval prediction, daily energy management and energy scheduling.