基于粗糙集理论与D-S证据理论改进的多元回归负荷预测方法研究

摘要

Nowadays, multivariate regression algorithm is mostly adopted in the mid-long term load forecasting, but it lacks good theoretical foundation for the selecting of the impact factors and the past years when modeling. It is difficult to make a balance between considering more affecting factors, historical data and reducing the regression model error, which leads to the inaccuracy of the multivariate regression algorithm in actual load forecasting. Rough set theory and D-S evidence theory are applied to the multivariate regression algorithm. First, rough set theory is used to sort the importance of influencing factors, and then the impact factors and the historical years are clustered respectively, so that several multiple regression models can be built. Additionally, the weights of different models are fused by using D-S evidence theory. In this way, the final combination forecasting model based on the multivariate regression analysis method can be built. According to the valid example, it can be concluded that the final model can preferably balance the selection of impact factors and past years while effectively improve the accuracy of the multiple regression algorithm in the mid-long term load forecasting, which provides stronger applicability at the same time.%当前，中长期负荷预测大多采用多元回归算法，但在建模时对影响因子及历史年的选择缺乏良好的依据，很难在考虑更多影响因子及历史年数据与降低回归模型误差之间做出平衡。这使多元回归算法在实际负荷预测中的精准度很不稳定。将粗糙集理论与D-S证据理论引入多元回归算法，利用粗糙集理论对影响因子进行重要性排序。分别以历史年和影响因子为对象进行聚类，以此建立多个多元回归模型。利用D-S证据理论对多个组合预测的权重分配方案进行权重融合，得出最终基于多元回归分析法的组合预测模型。经算例验证，该模型能较好地平衡影响因子和历史年的选取，能有效提高多元回归算法在中长期负荷预测中的准确性，适用性强。