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中文摘要：
该文引入信息理论分析电力系统的负荷预测以及可再生能源出力预测,将其描述为信息决策过程,并提出了短期负荷预测中的最小信息损失（minimization of information loss,MIL）综合模型,利用历史负荷与预测误差的分布情况在信息损失最小的原则下求解最可能的负荷取值。针对MIL综合模型中概率分布的估计问题,文中应用了正态分布参数估计和Parzen窗估计2种不同的方法,给出了各自的算法和实现方案。算例部分通过用实际电网负荷数据和实际风力发电出力数据进行测试,研究了MIL综合模型结构与参数对预测结果的影响,并在与传统综合模型的比较中显示了新模型的优越性。
英文摘要：
Information theory was introduced to analyze load forecasting of power systems, which was described as a process of information decision-making. A hybrid model for short-term load forecasting based on the principle of MIL（minimization of information loss） was presented. This MIL hybrid model utilized the distributions of historical load data and the corresponding forecasting errors of each single forecasting method to determine the most probable load values under the principle of MIL. To resolve the problem of probability estimation, two different techniques were proposed and implemented, including normal parametric estimation and kernel estimation. Through case studies on practical load data, the effects of the model structure and parameter on the forecasting results were investigated, and the advantage over conventional hybrid model was also displayed.
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