Abstract
In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.
Highlights
Smart cities are the development goals of many countries around the world [1]
Due to the details of support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) were introduced in many previous kinds of literature [34,35,36,37,38,39,40,41,42]; so, some description of them were added in the present study
It is of interest to consider the feasibility of the proposed Particle Swarm Optimization (PSO)-XGBoost model in estimating the heating load (HL) of buildings systems
Summary
Smart cities are the development goals of many countries around the world [1]. Intelligent systems have been widely researched and applied in smart cities to provide a better quality of life, as well as to bring higher economic efficiency [2,3,4,5,6]. One of the critical issues of smart cities is the efficient use of energy by buildings [7,8,9]. Energy for cooling or heating the buildings is significant since they take a considerable part of the total energy [10]. The demand for energy for the heating load (HL) is significant [11]. The ineffective use of HL results in economic losses and a threat.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.