基于长短期记忆网络的售电量预测模型研究

售电量预测对优化供电结构以及了解经济走势具有重要意义,然而,传统售电量预测方法难以从售电量及其影响因素的数据中自动抽取到较好的数据特征。为此,文中提出一种基于长短期记忆网络的售电量预测模型,该模型通过分析售电量数据及其影响因素的相关性,提出一种行业聚类方法,该方法根据不同行业的数据特征对相似的行业进行聚类,并根据聚类结果训练长短期记忆网络模型。文中模型能够学习售电量数据以及相关影响因素的数据特征和内在关联关系。实验结果表明,文中所提出的预测模型比经典的预测模型具有更高的准确度。

Electricity Sales Forecasting Based on Long-short Term Memory Networks

It is very important for electricity sales forecasting to optimize electricity supply structure and to understand economic trends.However,traditional electricity sales forecasting models cannot extract good enough data features from electricity sales data,which will generally result in lower forecasting accuracy.In this paper,a novel electricity sales forecasting model based on long-short term memory (LSTM) network is proposed.This model can analyze the correlation between electricity sales data and the related data.The original data are firstly clustered according to the features from different industries.Then,the LTM network is trained with the clustered data results.This model can automatically learn much better data features and intrinsic relationships between electricity sales data and other related data.Experimental results show the proposed model has achieved much higher forecasting accuracy than the traditional forecasting models.