基于偏最小二乘回归的中长期电力负荷预测

为了支持将来的经济发展和不断满足电力需求,负荷预测已成为电力部门的重要任务,而提高预测精度是负荷预测的关键问题。为此,判断了影响负荷的经济因素之间存在的多重共线性,用偏最小二乘回归方法消除其共线性影响,并建立了预测模型。结果表明,该方法能准确地估计出变量的回归系数,能避免使用普通最小二乘回归时出现的异常回归系数,预测的相对误差平均为9.83%,最小相对误差为-0.01%。

Medium and Long Term Load Forecasting Based on Partial Least-Squares Regression

To support economic growth and meet power requirement continually in the future, load forecasting has become a very important task for electric utilities,and the forcasting precision is the key problem.The multiple-colinearity among economic factors influencing load is judged and the effect of multiple-colinearity is weakened through partial least-square regression.Then a forecasting model is established.Results show that the proposed method can well estimate the variable's regression coefficients and avoid the abnormal regression coefficionts while using conventional least-square regression.The mean relative error of forcastng is 9.83%,and the smallest is-0.01%.