制冷技术2025，Vol.45Issue(2)：56-62,70,8.DOI:10.3969/j.issn.2095-4468.2025.02.201
基于改进长短期记忆神经网络方法的商场冷水机组系统短期能耗预测
Short-Term Energy Consumption Prediction of Chiller System in Shopping Mall Based on Improved Long Short-Term Memory Neural Network Method
摘要
Abstract
Aiming at the energy consumption prediction problem of an air-conditioning system in a shopping mall in Wuhan,the data mining method is used for modeling processing,in which the Savitzky-Golay smoothing algorithm is introduced to perform noise reduction processing on the original data,and the long short-term memory neural network algorithm is used to predict and analyze the instantaneous energy consumption.The results show that compared with methods such as back-propagation neural network,recurrent neural network and gated recurrent unit,the long short-term memory neural network has highest prediction accuracy,in which the determination coefficient is 0.861.The denoising of raw data using the Savitzky-Golay smoothing algorithm significantly enhances prediction accuracy by reducing the influence of noise,achieving a coefficient of determination of 0.955 with a increasing of 10.9%.The feasibility of the proposed method for energy consumption prediction in chilled water systems of commercial buildings is thereby validated.关键词
能耗预测/冷水机组/数据挖掘/数据降噪Key words
Energy consumption prediction/Chillers/Data mining/Data noise reduction引用本文复制引用
许源驿,肖楚鹏,黎强,陈焕新,程亨达..基于改进长短期记忆神经网络方法的商场冷水机组系统短期能耗预测[J].制冷技术,2025,45(2):56-62,70,8.基金项目
国家自然科学基金(No.51876070). （No.51876070）
