基于RWT-SVM的台区配电网日前负荷预测研究

日前负荷预测对于制定合理的调度计划，保证电力系统安全可靠具有重要意义.电力负荷时间序列通常存在随机误差，而基于智能算法的预测模型为了充分提取负荷信息，结构复杂、计算量大.为此，本文利用台区配电网的历史电力负荷时间序列，提出一种基于重复小波变换-支持向量机（RWT-SVM）混合模型的日前电力负荷预测方法.该方法利用小波变换技术将台区配电网电力负荷时间序列分解为多个子序列；利用平均绝对误差（MAE）计算每个子序列的预报误差贡献度；对MAE最大的序列进一步分解，从而提升模型的预测能力，得到精度更高的预测结果.仿真结果表明，RWT-SVM混合模型的预测精度高于三种对比方法.

Day-ahead load forecasting of distributed power grids based on RWT-SVM

Day-ahead load forecasting is an important task for the power dispatching center to formulate reasonable dispatching plans thus to ensure the safety and reliability of power system operation.However, random errors exist in time series of power loads, and the intelligent algorithm based prediction models are complex in structure and incapable of fully extracting load information enough for load calculation and load forecasting.Here, we propose a day-ahead power load forecasting approach based on Repeated Wavelet Transform-Support Vector Machine (RWT-SVM) by using the historical power load time series of distributed power grids.The approach uses wavelet transform to decompose the power load time series of distributed power grids into multiple subsequences, then applies the Mean Absolute Error (MAE) to calculate the prediction errors contributed by each subsequence, and further decomposes the sequence with the largest MAE to improve the prediction ability of the model.The simulation results show that the proposed RWT-SVM approach outperforms other methods in forecasting accuracy.