摘要
短期日电量预测有助于电力市场建设和供电服务水平提升。基于用户特征的准确辨识,提出了一种行业用户短期日电量的二重分解预测方法。首先运用统计学工具分别对用户的周期性、气温相关性和节假日相关性等特征进行辨识建模;然后据此定制基于回归的季节趋势分解框架的分量数量;接着结合回归周期分解法和集合经验模态分解法对电量进行二重分解;进而依据分量特点选用长短期记忆网络/支持向量机/卷积神经网络组合预测;最后将各分量预测结果叠加并利用概率分布调整节假日预测结果,从而得到最终的日电量预测值。实际算例分析验证了所提方法在预测精度上的优越性。
Short term daily electricity forecasting is helpful to construct power market and improve power supply service level.With the help of the accurate identification of different users’ characteristics,a short-term daily electricity demand prediction modeling of industrial users based on doubly decomposition is proposed.Firstly,statistical tools are used to identify the seasonality,temperature correlation and holiday correlation of users;Then,according to the recognition results,the seasonal-trend decomposition procedure based on regression is customized;After that,the electricity demand is decomposed by doubly decomposition combined with the regression cycle method and the ensemble empirical mode decomposition;Next,based on the characteristics of the obtained components,long/short term memory networks,weighted least squares support vector machine and convolutional neural network is used for prediction;Finally,the prediction results of each component are superimposed,and the holiday prediction results are adjusted by using the probability distribution to obtain the final prediction value.Through the analysis of practical examples,the proposed method has high prediction accuracy.
作者
黄国权
严玉婷
李晖
张勇军
HUANG Guoquan;YAN Yuting;LI Hui;ZHANG Yongjun(School of Electric Power Engineering,South China University of Technology,Guangzhou 510641,China;Shenzhen Power Supply Bureau Co.,Ltd.,Shenzhen,Guangdong 518001,China;Electric Power Research Institute,Guangdong Power Grid Co.,Ltd.,Guangzhou 510080,China)
出处
《南方电网技术》
CSCD
北大核心
2022年第11期37-45,共9页
Southern Power System Technology
基金
国家自然科学基金资助项目(52177085)。
关键词
行业用户
电量预测
智能算法
二重分解
industrial users
electricity demand prediction
intelligence algorithm
doubly decomposition
