复杂环境下新型电力系统扩展短期负荷预测研究

Study on Short-term Load Forecasting for Expansion of New Power System in Complex Environments

由于能源结构的多元化和用电需求的多样性,电力系统的运行环境和负荷变得越发复杂,导致短期负荷预测精度低。针对上述问题,提出复杂环境下新型电力系统扩展短期负荷预测。通过分解大量的历史负荷数据,发现负荷变化的多种趋势和周期性特征。计算负荷序列的形态系数,反映负荷序列的动态变化。选取趋势相似日找到与目标预测日气候条件、节假日安排等相似的历史日,在得到相似日的负荷数据后,对数据进行处理和参数优化,进而预测目标日的负荷。实验结果表明,该预测方法预测精度较高,最终能输出较为准确的短期负荷预测结果。

Due to the diversification of energy structure and electricity demand,the operating environment and load changes of the power system have become increasingly complex,leading to low accuracy in short-term load forecasting.To address the above issues,a study on short-term load forecasting for the expansion of new power systems in complex environments is carried out.By decomposing a large amount of historical load data,multiple trends and cyclical characteristics of load changes are discovered.The type coefficient of the load series is calculated to reflect the dynamic changes of the load series.The load data of historical days which have similar trend with climate conditions and holiday arrangements are obtained,processed and optimized to predict the load for the target day.The experimental results show that the prediction method has high prediction accuracy and can ultimately output more accurate short-term load forecasting results.