基于最大信息熵原理的短期负荷预测综合模型

A COMBINED MODEL FOR SHORT TERM LOAD FORECASTING BASED ON MAXIMUM ENTROPY PRINCIPLE

引入了信息理论来研究和处理负荷变化的不确定性, 提出了基于最大信息熵原理的短期负荷预测综合模型,该模型将各种单一预测模型的预测结果以及历史预测误差分布作为约束信息,利用最大熵原理得到预测结果的分布。文中阐述了新模型的应用背景、思路和理论,给出了具体的实现方案和算法,并在实际电网中得到了应用。针对实际电网的算例研究表明,对于随机性较大的电网负荷,传统综合预测模型存在明显的过拟合现象,而新模型则有效地提高了预测精度。

As uncertainty exists in power system load demand, information theory is introduced to deal with the uncertainty in load forecasting. In this paper, a novel combined short time forecasting model based on maximum entropy principle is proposed. Taking the forecasting values and the historical forecasting error distributions produced by all the adopted individual load forecasting models as constraints, the new combined model can give out a probability distribution of forecasting value based on maximum entropy principle. The background, theory and implementation details of the new combined model are also presented. Application results in a real-life utility show that overfitting problem arising in the traditional combined models is overcomed by the proposed model, and the forecasting precision is improved by the proposed model when load demand is more stochastic.