Abstract
In order to improve the precision of cooling load prediction, the authors of this essay proposes neural network model based on EDA-PSO-BP algorithm. We used PSO optimization algorithm combined with BP neural network to do cooling load prediction experiments of indoor sample data of a building. The results showed that compared with other three kinds of prediction algorithms, the error of this algorithm is minimum and its running speed is the fastest.
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Huang, Z., Yan, L., Peng, X., Tan, J. (2016). Short-Term Forecasting and Application About Indoor Cooling Load Based on EDA-PSO-BP Algorithm. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_12
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DOI: https://doi.org/10.1007/978-3-319-45835-9_12
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