基于贝叶斯方法与可解释机器学习的负荷特性分析与预测

Characteristic Analysis and Load Forecasting Based on Bayesian Method and Interpretable Machine Learning

使用机器学习模型和方法进行短期负荷预测,虽能提升负荷预测的整体精度,但在极端天气、节假日等小样本预测场景中,对比基于专家经验的人工预测无明显优势。为充分结合预测业务人员的经验知识与机器学习的推理泛化能力,提出了一种基于贝叶斯时变系数(BTVC)与CatBoost模型的可解释负荷预测框架。首先,结合数据与专家知识,构建BTVC模型进行预测,获得各影响因子、趋势及周期因素的负荷分量。其次,将上述结果与常规特征进行组合,作为CatBoost回归模型的输入,进行最终预测。然后,使用事后模型解释框架(SHAP)进行归因分析,框架输出的定量关系可供负荷预测业务人员参考,使其开发出更有效的特征,进一步提高预测效果。最后,以某地区实际电网负荷数据为例,验证所提负荷预测与结果分析框架的有效性。

Using machine learning models and methods for short-term load forecasting can improve the overall accuracy of load forecasting,but in small sample forecasting scenarios,such as extreme weather conditions and holidays,there is no obvious advantage over manual forecasting based on expert experience.In order to fully combine the expert knowledge of forecasting operators with the reasoning generalization ability of machine learning,an interpretable load forecasting framework based on Bayesian time-varying coefficient(BTVC)and CatBoost model is proposed.First,combined with data and expert knowledge,BTVC model is built to forecast,and load components of various influencing factors,trends and seasonality factors are obtained.Secondly,combine the above results with conventional features as the input of CatBoost regression model for final forecasting.Then,Shapley additive explanation(SHAP)is used for attribution analysis.The quantitative relationship of the framework output can be used as a reference for load forecasting practitioners to create more effective features to further improve the forecasting results.Finally,the proposed load forecasting and result analysis framework are validated by using real load data in power grid of a region as an example.