网格化的电力系统短期负荷预测的MDRBR模型

针对大电网的短期负荷预测,建立了按地域划分的网格化电力系统短期负荷预测模型.各子网格根据自身的历史负荷和气象条件建立对网格更为有效的负荷预测模型,并采用了面向粗糙集的默认规则挖掘算法(MDRBR--mining default rules based on rough set)构造各单一预测模型,从而获得更加准确的预测结果.文中首先描述了MDRBR算法,然后分析研究了网格化的日负荷多层规则网络构造过程,并给出了基于MDRBR算法的日负荷预测过程以及对某地历史数据的负荷预测结果.分析结果表明,该网格化负荷预测模型能更加准确地得出预测结果,有效地减少噪声,计算简单,且规则搜索效率高.

Grid Model for Short-term Load Forecasting Based on MDRBR Algorithm

This paper presents a grid model in terms of geographical division for short-term load forecasting in a bulk power system. The subnet model in each geographical grid, considering its own historical loads and meteorological conditions, is more effective and could lead to more accurate results. Therefore, every subnet model is established based on the mining default rules based on rough sets (MDRBR) algorithm. First, the MDRBR algorithm is discussed, and the constructing process of the multi-layered rule-network of daily load forecasting is then analyzed in detail. Furthermore, the whole process of load forecasting based on the MDRBR algorithm is presented. Finally, an example using actual historical data shows that the grid forecasting model can yield high accurate results, reduce noises effectively, and is efficient in computation and rule searching.