相空间重构和SVR耦合的短期电力负荷预测

电力系统的短期负荷预测精度对智能电网安全运行有着重要影响,其中预测精度和训练步数至关重要,目前当地气象因素逐渐成为负荷预测中的关注点。以某市短期电力负荷为研究对象,建立了考虑日特征相关因素的支持向量回归机短期电力负荷预测模型,随后对某市考虑气象及日期类型的电力负荷做出预测。研究表明:利用考虑实时气象因素的SVR预测模型对短期电力负荷进行预测精度较高;考虑气象及日期类型的预测误差比不考虑气象及日期的预测误差小;嵌入维数和时间延迟对负荷预测模型精度具有重要影响。

Short-term load forecasting based on phase space reconstruction and SVR coupling model

The short-term power load forecasting of the power system is an important part of the power grid management system. The prediction accuracy directly affects the stable and safe operation of the power grid, and the weather becomes more and more important in the load forecasting. This paper takes a city load as an example to study the short-term load forecast, and establishes the SVR prediction model which consider the relevant factors of daily characteristics. The results show that the SVR forecasting model considering real-time meteorological factors has higher forecasting accuracy for short-term power load. The forecasting error considering meteorological and date type is smaller than that without considering meteorological and date. The embedded dimension and time delay have greater impact on the accuracy of the forecasting model. The research results can provide reference for short-term load forecasting of power grid.