结合用户画像的DTW-MANN-FM分布式光伏短期出力预测模型

DTW-MANN-FM MODEL COMBINED WITH USER PROFILE FOR DISTRIBUTED PHOTOVOLTAIC POWER SHORT-TERM FORECASTING

为解决分布式光伏短期预测中发电户特性差异、地理位置偏移导致气象数据偏差的问题,并进一步提升算法预测性能,提出结合用户画像的动态时间规整(dynamic time warping,DTW)-多头自注意力神经网络(multi-head attention neural network,MANN)-因子分解机(factorization machine,FM)预测模型。首先分析发电户档案数据和历史发电数据,统计出用户画像;再结合基于DTW标准气象特征修正偏移算法,形成合理、完善的“用户+气象”特征组合样本;最后使用加权的数据样本对模型进行训练。仿真阶段使用江苏省真实光伏、气象数据,将所提模型与当前业界先进的若干光伏预测模型进行对比实验,结果表明该模型具有更高的准确度和鲁棒性,表现出更佳的预测性能。

In order to solve the problem of generator characteristic difference and meteorological data deviation caused by geographical location offset in distributed photovoltaic power short-term forecasting,further improving the prediction performance,a dynamic time warping(DTW)-multi-head attention neural network(MANN)-factorization machine(FM)prediction model combined with user profile is proposed.Firstly,we analyze the profile data and historical power generation data of generators to count the user profile.Then we use the optimization algorithm based on DTW standard,to correct offset of meteorological characteristics,forming a reasonable and perfect“user+meteorological”feature combination.Finally we utilize the weighted data samples to train the model.In the simulation stage,the real photovoltaic data of Jiangsu Province are used to compare the proposed model with several advanced photovoltaic prediction models in the industry.The results show that the proposed model has higher accuracy and robustness,indicating better predictive performance.