基于气象成分分解的夏季短期负荷预测

夏季负荷受温度等气象因素影响大,表现出随机性强、波动性大的特点。针对现有短期负荷预测模型在夏季预测精度不高的问题,提出在负荷成分分解的同时,将温度分解为日周期分量和波动分量,以此准确把握短时气象波动对夏季短期负荷预测的影响。在充分分析负荷各分量变化趋势及对整体负荷预测精度影响的基础上,针对各个负荷分量特征分别选择预测方法。在预测气象敏感负荷分量时引入温度波动分量,基于XGBoost智能算法构建预测模型。选用我国中部某市夏季历史负荷建立训练样本,对2017年8月份日96点负荷进行预测,预测结果验证了所提模型和算法的有效性。

Short-term load forecasting in summer based on meteorological factors decomposition

The summer load is greatly affected by meteorological factors such as temperature, showing the characteristics of strong randomness and large fluctuation. To solve the problem of the low short-term load forecasting precision of the existing models in Summer, it is proposed to decompose the temperature into daily periodic components and fluctuation components while the load components are decomposed, which is beneficial to accurately grasp the impact of short-term weather fluctuations on short-term load forecasting. After analyzing the variation feature of each load component and its impact on the forecasting accuracy of the overall load, the forecasting method for each load components is designed respectively according to their different feature. Taking the temperature fluctuation components into consideration while forecasting the weather-sensitive load, a short-term load forecasting model is constructed based on the XGBoost algorithm. The historic summer load of a middle city of China is chosen to establish the training samples, the results of 96 time points load in August 2017 show the proposed forecasting model and algorithm are effective.