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.
ASJC Scopus subject areas