基于宽度学习系统的短期电力负荷预测方法研究

电力负荷预测已成为电力调度的一项重要工作,也是评估电力企业是否实现现代化的重要指标之一。精准可靠的电力负荷预测数据对合理安排电力企业发电机组启停、降低电力损失、保障社会用电安全和提高电力企业经济效益等方面具有重要意义。短期电力负荷预测是针对短期负荷变化、甚至实时负荷变化,但由于短期负荷变化较为突然,预测难度大。为了提高短期电力负荷预测精确度,本文提出了一种基于宽度学习系统的短期电力负荷预测方法,通过采用邻域粗糙集分类算法,对输入参数进行特征提取,然后采用宽度学习系统对电力负荷历史数据进行离线训练,利用已训练完成的模型实现24h短期负荷预测。研究结果表明,使用本方法可以有效降低MAPE和RMSE,也有效减少了训练时间,提高了模型训练速度,具有优异的预测能力。

Research on Short-term Power Load Forecasting based on the Broad Learning System

Electricity load forecasting has become an important task in power dispatching and one of the important indicators to measure whether the power enterprise is modernized or not. Accurate power load forecasting is of great significance in reasonably arranging the start and stop of generating units of power enterprises, reducing excess power loss, improving economic efficiency and guaranteeing the social demand for electricity. Short-term load forecasting is aimed at short-term load change or even real-time load change, but it is difficult to forecast because short-term load change is more abrupt. In this paper, we propose a short-term power load forecasting method based on the broad learning system(BLS), by using the neighborhood rough set classification algorithm to extract features from the input parameters. Then, the trained model is used to achieve 24h short-term load forecasting by using the offline training of historical data of electric load using BLS. The results show this method using the broad learning system effectively reduce MAPE and RMSE, also reduce the training time, improve the model training speed, and have excellent forecasting capability.