具有自学习功能的电力负荷模糊推理预测

Fuzzy Inference Forecasting of Power Load with Self Learning Function

考虑到电力负荷预测问题中存在的不确定性，采用模糊推理预测方法进行负荷预测的研究．该方法利用电力最高负荷和国内生产总值的历史数据建立模糊推理规则，从而进一步建立最高负荷跟国内生产总值之间的模糊蕴涵关系，通过模糊推理得到负荷预测结果．为了使模型能够象专家一样不断地总结新的经验并调整原有的经验，模型克服了其它模型中知识的凝固性，具有自学习功能．利用某地区的实际数据进行分析计算，得到了满意的结果．

Because of the existence of the indefinite factors in power load forecasting, a new load forecasting model — the fuzzy inference forecasting is presented. The history data is used to set up the fuzzy inference rules and then the fuzzy relation. The fuzzy inference methods are used to get the results of load forecasting. This model can absorb the new information constantly, overcome the knowledge solidity usually existing in the other models, and has the self learning function. A set of real data is used, and the satisfactory results are obtained.