加权Markov修正模糊信息粒的电力负荷区间预测

针对电力负荷预测存在波动性且预测精度不高的问题，提出一种基于加权马尔可夫(Markov)修正模糊信息粒的电力负荷区间预测方法.该方法首先对电力负荷数据序列进行基于模糊信息粒化(FIG)的空间窗口重构，以此得到电力负荷模糊信息粒和电力负荷的各阶自相关系数；然后建立由基于FIG和长短时记忆网络(LSTM)组合的模型(FIG-LSTM),以此获得能够预测不同模糊粒的3组LSTM模型；最后建立加权Markov-FIG-LSTM模型，并通过消除3组LSTM模型中的预测误差得到电力负荷预测区间和趋势值.实例分析表明，Markov-FIG-LSTM模型的RMSE、MAE和MAPE指标比FIG-LSTM模型分别降低了4.78%、11.37%和11.72%,因此该方法可为电网调度提供有效的数据支撑.

Power load interval forecast based on weighted Markov modified fuzzy information granule

Aiming at the problem of fluctuation and low precision in power load forecasting, a method of power load interval prediction based on weighted Markov modified fuzzy information granules was proposed.Firstly, the spatial window of power load data series is reconstructed based on fuzzy information granulation(FIG), and the order autocorrelation coefficients of fuzzy information granules and power load are obtained.Then, a combination model(FIG - LSTM)based on FIG and LSTM was established to obtain three groups of LSTM model that could predict different fuzzy particles.Finally, the weighted Markov - FIG - LSTM model is established, and the power load prediction interval and trend value are obtained by eliminating the prediction error of three groups of LSTM models.The example analysis shows that the RMSE, MAE and MAPE indexes of the Markov - FIG - LSTM model are reduced by 4.78%, 11.37% and 11.72% respectively compared with the FIG - LSTM model.Therefore, this method can provide effective data support for power grid dispatching.