基于随机森林算法的短期电力负荷预测

Short term power load forecasting based on a stochastic forest algorithm

为了准确预测电力系统的短期负荷变化,为电力系统安全、经济、高效运行提供指导方向,提出了一种将模糊聚类以及随机森林回归算法进行组合的电力系统负荷预测方法,利用粗糙集构建补偿规则,对预测结果进行修正补偿。首先,通过对电力系统负荷的周期性、天气相关性等特征进行分析,利用C均值模糊聚类算法对历史样本进行聚类,在进行随机森林回归预测时,使用聚类后同类数据作为训练集样本构建决策树。考虑到随机森林回归预测偏保守、电力系统负荷在峰值处波动大的特征,在得到预测结果后利用粗糙集理论生成补偿规则,对负荷预测进行修正。利用所述方法对北爱尔兰地区进行一日24 h的负荷预测,结果跟实际负荷的平均绝对误差百分比为2.09%,验证了该预测方法的有效性。

In order to accurately predict the short-term load change of a power system and provide guidance for safe,economic and efficient operation,a load forecasting method based on fuzzy clustering and random forest regression is proposed.A rough set is used to construct the compensation rules,and the prediction results are modified and compensated for.First,this paper analyzes the periodicity and weather correlation of power system load.Historical samples are clustered using C-mean fuzzy clustering.In the random forest regression prediction,similar data after clustering is used as a training set sample to build a decision tree.Taking into account the conservatism of partial random forest regression prediction and large fluctuations of power system load at the peak,the rough set theory is used to generate compensation rules after the prediction results are obtained,and load forecasting is modified.The 24-hour load forecasting of the Northern Ireland region using the above method shows that the Mean Absolute Percentage Error(MAPE)is 2.09%compared with the actual load,which verifies the effectiveness of the forecasting method.