基于鲸鱼优化算法的高效制冷机房负荷预测研究
Research on the load forecast of high-efficiency refrigeration room based on whale optimization algorithm
潘亚男 1崔红社 1刘高伟1
作者信息
- 1. 青岛理工大学环境与市政工程学院,青岛 266525
- 折叠
摘要
为了准确预测建筑冷负荷,降低机房能耗,提出了基于鲸鱼优化算法(WOA)的BP神经网络预测模型.基于青岛办公建筑的实际历史运行数据,建立了 BP神经网络、基于粒子群寻优的BP神经网络(PSO-BP)、基于遗传算法改进的BP神经网络(GA-BP)、基于鲸鱼优化算法的BP神经网络(WOA-BP)4种负荷预测模型,并比较了 4种负荷预测模型的结果.研究表明,短期预测内,WOA-BP神经网络预测模型的最大百分误差为-15.76％,最小百分误差为-0.03％,平均绝对值百分误差为6.60％.与其他模型相比,WOA-BP模型具有更高的预测精度.
Abstract
In order to accurately forecast the cooling load of buildings and reduce the energy consumption of the refrigeration room,a BP(Back Propogation)neural network forecasting model based on the whale optimization algorithm(WOA)is proposed in this paper.Based on the actual historical operational data of office buildings in Qingdao,four load forecasting models were established,namely BP neural network,BP neural network based on particle swarm optimization(PSO-BP),BP neural network improved by genetic algorithm(GA-BP)and BP neural network based on whale optimization algorithm(WOA-BP).A comparative analysis of the results from these four load forecasting models was conducted.The research shows that in the short-term forecasting,the maximum percentage error of WOA-BP neural network forecasting model is-15.76％,the minimum percentage error is-0.03％,and the average absolute percentage error is 6.60％.Compared with the other models,WOA-BP model has higher forecasting accuracy.
关键词
建筑节能/鲸鱼优化算法/负荷预测/BP神经网络Key words
building energy saving/whale optimization algorithm(WOA)/load forecast/Back Propagation neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据