TY - JOUR
T1 - A backpropagation neural network improved by a genetic algorithm for predicting the mean radiant temperature around buildings within the long-term period of the near future
AU - Xie, Yuquan
AU - Ishida, Yasuyuki
AU - Hu, Jialong
AU - Mochida, Akashi
N1 - Funding Information:
This study was supported by a Grant-in-Aid for Challenging Research (Exploratory) (No. 19K22004) and the China Scholarship Council (No. 201708430100).
Publisher Copyright:
© 2021, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/3
Y1 - 2022/3
N2 - This study aimed to develop a neural network (NN)-based method to predict the long-term mean radiant temperature (MRT) around buildings by using meteorological parameters as training data. The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort. In NN-based long-term MRT prediction, two main restrictions must be overcome to achieve precise results: first, the difficulty of preparing numerous training datasets; second, the challenge of developing an accurate NN model. To overcome these restrictions, a combination of principal component analysis (PCA) and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy. Second, three widely used NN models (feedforward NN (FFNN), backpropagation NN (BPNN), and BPNN optimized using a genetic algorithm (GA-BPNN)) were compared to identify the NN with the best long-term MRT prediction performance. The performances of the tested NNs were evaluated using the mean absolute percentage error (MAPE), which was ≤ 3% in each case. The findings indicate that the training dataset was reduced effectively by the PCA and K-means. Among the three NNs, the GA-BPNN produced the most accurate results, with its MAPE being below 1%. This study will contribute to the development of fast and feasible outdoor thermal environment prediction.
AB - This study aimed to develop a neural network (NN)-based method to predict the long-term mean radiant temperature (MRT) around buildings by using meteorological parameters as training data. The MRT dramatically impacts building energy consumption and significantly affects outdoor thermal comfort. In NN-based long-term MRT prediction, two main restrictions must be overcome to achieve precise results: first, the difficulty of preparing numerous training datasets; second, the challenge of developing an accurate NN model. To overcome these restrictions, a combination of principal component analysis (PCA) and K-means clustering was employed to reduce the training data while maintaining high prediction accuracy. Second, three widely used NN models (feedforward NN (FFNN), backpropagation NN (BPNN), and BPNN optimized using a genetic algorithm (GA-BPNN)) were compared to identify the NN with the best long-term MRT prediction performance. The performances of the tested NNs were evaluated using the mean absolute percentage error (MAPE), which was ≤ 3% in each case. The findings indicate that the training dataset was reduced effectively by the PCA and K-means. Among the three NNs, the GA-BPNN produced the most accurate results, with its MAPE being below 1%. This study will contribute to the development of fast and feasible outdoor thermal environment prediction.
KW - K-means clustering
KW - backpropagation neural network
KW - genetic algorithm
KW - long-term prediction
KW - mean radiant temperature
KW - principal component analysis
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U2 - 10.1007/s12273-021-0823-6
DO - 10.1007/s12273-021-0823-6
M3 - Article
AN - SCOPUS:85114147528
VL - 15
SP - 473
EP - 492
JO - Building Simulation
JF - Building Simulation
SN - 1996-3599
IS - 3
ER -