计及分布式光伏电源出力影响的母线净负荷预测

母线负荷波动性强、易受用户用电行为的影响,接入分布式光伏电源(Distribution Generator,DG)后,其出力波动会进一步增加母线净负荷不确定性。针对此问题,提出以随机森林(Random Forest,RF)作为预测器,分别预测光伏DG出力与母线负荷的母线净负荷预测新方法。文章构建含气象与社会信息等因素在内的高维原始特征集合,并以原始特征集合分别构建光伏DG出力与母线负荷RF预测器。在RF训练过程中,以PI值分析原始特征集合各特征重要度并排序;以不同维度特征子集RF模型预测准确率作为决策变量,采用前向特征选择法,确定最优特征子集,并构建最优预测器;最后,以母线负荷预测值减去光伏DG出力获得母线净负荷预测值。以某地区实际含光伏电源母线数据开展实验,验证了新方法的有效性与先进性。

Pure Bus load prediction of electrical bus considering influence of distributed photovoltaic power output

The bus load is highly volatile and susceptible by the influence of users" electricity consumption behavior. Affected by the Distribution Generator (DG), the uncertainty of net load of the bus will further increase. In order to solve this problem, a new method of net bus load prediction using Random Forest (RF) as a predictor to predict photovoltaic DG output and bus load respectively is proposed. Firstly, the high-dimensional original feature set including meteorological, social information and other factors is constructed. RF predictors of photovoltaic DG output and bus load are constructed respectively based on the original feature set. In the training process of RF, the importance of each feature in original feature set was analyzed and sorted by PI value. Secondly, RF model prediction accuracy of characteristic subset of different dimensions is taken as the decision variable,