论文--中国科学院地理科学与资源研究所

论文

论文题目：	Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms
第一作者：	Xu Yuanjin, Li Fei, Asgari Armin
联系作者：
发表年度：	2022
摘　　要：	Since cooling and heating loads are regarded as significant parameters to examine the energy performance of buildings, the need to predict and analyze them for the residential buildings seems to be undeniable. Hence, the present paper wants to optimize the multi-layer perceptron neural network using several optimization methods to predict the heating and cooling of energy-efficient buildings. The dataset used in this study consists of eight independent factors: surface area, wall area, roof area, relative compactness, overall height, orientation, glazing area, and glazing area distribution. To prove the reliability and accuracy of the obtained results, test and training data are also considered. According to the statistical results, biogeography-based optimization has the highest value of R-2 and the lowest values of RMSD, normalized RMSD, and MAE in both training data and test data for cooling and heating loads. Hence, the forecasting accuracy of the proposed MLP neural network optimized with the BBO optimization algorithm with the RMSD, R-2, and MAE of 2.82, 0.920, 2.15 in the training phase of heating load and with the RMSD, R-2, and MAE of 3.18, 0.887, 2.97 in the training phase of the cooling load is much better than those of the other models. (C) 2021 Elsevier Ltd. All rights reserved.
英文摘要：
刊物名称：	ENERGY
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论文类别：	SCI