中长期电力组合预测模型的理论研究

Theoretical Study of Combination Model for Medium and Long Term Load Forecasting

传统中长期电力负荷组合预测方法中权系数依赖预测方法,预测模型不能很好地反映负荷发展变化规律,对此,提出了基于诱导有序几何加权平均算子(induced ordered weighted geometry averaging operator,IOWGA)和加权马尔可夫链的新型组合预测模型。该模型根据每个单项预测方法在各年份上预测精度的高低按顺序对其赋权,摆脱了权重对各预测方法的依赖,实现了组合预测模型中权系数与拟合精度在任一时点上的相关性。通过加权马尔可夫链定性地推测出预测年份上各单项预测方法的预测精度状态,确定其在预测年份的权系数进而预测。算例分析表明,该组合预测模型能够较好地模拟负荷发展规律,提高预测精度,具有较高的实用价值。

For traditional combination methods of medium and long term power load forecasting, the weight coefficient is dependent on the prediction methods, so the model can not reflect the changes of load development. Therefore, a new combination model based on induced ordered weighted geometry averaging operator （IOWGA） and weighted Markov chain is proposed. According to the level of accuracy, this model assigns the weight to each individual method to achieve the correlation between weight coefficient and fitting accuracy in any time point. Since ordered weighted Markov chain has qualitatively forecasted the accuracy of each method of the target year, the weight coefficient can be determined for forecasting. Theoretical analysis shows that the new combination model fits the law of load development well and it helps to improve the forecasting accuracy with high practical value.