基于配用电大数据的短期负荷预测

Distribution and Consumption Big Data Based Short-term Load Forecasting

受限于数据信息的不完整和粗粒度,短期网供负荷预测的准确率一直难以进一步提升,而配用电信息系统数据的积累和大数据技术的快速发展为开展基于配用大数据的短期负荷预测提供了数据基础和技术支撑。本文首先针对配用电大数据的特征分析了"脏数据"的来源与类型,并提出了相应的数据清洗方法;其次基于大量的历史负荷、电量和气象数据,构建了行业负荷温度影响模型和行业电量节假日影响模型;最后基于上述用电影响模型开展了江苏电网短期网供负荷预测,实际计算结果验证了预测效果的有效性和准确性。

Limited to incompleteness of power load,electricity and other relevant data,the accuracy of network supply shortterm load forecasting is hard to enhance. The data accumulation of distribution and utilization system and rapid development ofbig data technology provide data basis and technical support for big data based load forecasting. Firstly,the sources and types of‘dirty data’are analyzed based on the characteristics of distribution and utilization big data,and the corresponding methods ofdata cleaning are put forward in this paper. Secondly,based on a large amount of historical power load,electricity consumptionand meteorological data,industry loadtemperature impact model,and industry electricityholiday impact model are established.Finally,shortterm load forecasting is carried out based on above models,and test cases show effectiveness and accuracy ofproposed big data based shortterm load forecasting method.