摘要
精准的多元负荷预测有助于综合能源系统的合理规划和优化运行。针对多元负荷预测时输入参数难确定和模型网络参数较难合理设置的问题,提出一种建筑电、冷、热多元负荷协同预测方法。首先,考虑到不同输入参数对多元负荷的影响,采用灰色关联度分析法(grey relation analysis,GRA)计算各输入参数与负荷间的相关性,选择灰色关联度大于0.6的参数作为模型输入;同时利用灰狼优化算法(grey wolf optimizer,GWO)对长短时记忆神经网络(long short-term memory,LSTM)中的关键网络参数进行优化,建立GRA-GWO-LSTM多元负荷预测模型;最后,以亚利桑那州立大学为例,通过与单一神经网络模型和混合神经网络模型GWO-LSTM对比,所提预测模型在电、冷、热负荷长期预测上具有更高的预测精度,较LSTM模型和GWO-LSTM模型的平均绝对百分比误差(mean absolute percentage error,MAPE)分别降低了31.64%和23.47%,且其对短期负荷预测也具有良好预测性能,可用于指导综合能源系统的规划和智能化运行。
Accurate multi-energy load forecasting is helpful to the rational planning and optimal operation of integrated energy systems.For solving the problems that it is very difficult to determine input parameters and set appropriate model network parameters in the multi-energy load forecasting model,a collaborative forecasting method for multi-energy load of building including electricity load,cooling load and heating load was developed.Firstly,considering the influence of different input parameters on multiple loads,the grey relation analysis(GRA)was used to calculate the correlation between input parameters and loads,and the parameters with values of grey relation greater than 0.6 were selected as the model inputs.At the same time,the grey wolf optimizer(GWO)was used to optimize the key network parameters of the long short-term memory neural network(LSTM),and the multi-energy load forecasting model of GRA-GWO-LSTM was developed.Finally,taking Arizona State University as an example,compared with the single neural network model and the hybrid neural network(GWO-LSTM)model,the proposed prediction model has higher prediction accuracy in long-term forecasting of electricity,cooling,and heating loads.Compared with the LSTM model and GWO-LSTM model,the mean absolute percentage error(MAPE)of the proposed prediction model is reduced by 31.64%and 23.47%,respectively.Furthermore,the GRA-GWO-LSTM model also has good predictive performance for short-term load forecasting and can be used to guide the design and intelligent operation of integrated energy systems.
作者
李文
卜凡鹏
王坤
高宇琪
时国华
LI Wen;BU Fan-peng;WANG Kun;GAO Yu-qi;SHI Guo-hua(China Electric Power Research Institute Co.,Ltd.,Beijing 100192,China;State Grid Tianjin Electric Power Company,Tianjin 300010,China;School of Energy Power and Mechanical Engineering,North China Electric Power University,Baoding 071003,China)
出处
《科学技术与工程》
北大核心
2024年第36期15518-15525,共8页
Science Technology and Engineering
基金
国家电网有限公司科技项目(5400-202212166A-1-1-ZN)。
