关键信息缺失下基于相空间重构及机器学习的电力负荷预测

随着碳交易系统的发展,准确预测电力能源消耗对于能源管理是至关重要的。为实现在缺失天气等多种关键信息下的电力负荷预测,首先采用混沌理论中的相空间重构技术对历史负荷时间序列进行处理,根据排列熵验证混沌特性。并利用8种机器学习模型进行预测与比较,其中包括4种以神经网络为基础的机器学习模型、3种以统计学习为基础的机器学习模型及1种基准模型。其次采用灰色关联度法对预测精度较高的极限学习机(ELM)和极端梯度提升(XGBoost)进行组合,构建了ELM-XGBoost模型。最后将ELM-XGBoost应用于一日至一周内不同时间尺度的负荷预测。结果表明,预测精度随预测时间尺度增加而呈现降低的趋势,且在日负荷预测中,所构建的ELM-XGBoost模型预测精度得到提升,应用效果良好。

Power load forecasting without key information based on phase space reconstruction and machine learning

With the development of the carbon trading system,accurate forecasting of power consumption is crucial for energy management.For power load forecasting without key information such as weather information,phase space reconstruction technique of chaos theory is adopted first to deal with historical load time series.Permutation entropy is used to validate the chaotic characteristic.8 kinds of machine learning models are used to forecast and make comparisons,These models are:4 kinds of neural network,3 kinds of statistical machine learning and 1 kind of benchmark.Secondly,two optimal models,extreme learning machine(ELM)and extreme gradient boosting(XGBoost),are combined by a grey relational degree method to construct an ELM-XGBoost model.Finally,ELM-XGBoost is applied to forecast with different time scales from one day to one week.Results show that forecasting accuracy decreases with the increase of forecasting time scale.In daily load forecasting,the accuracy of ELM-XGBoost model is improved with a better application effect.This work is supported by the National Key Research and Development Program of China(No.2020 YFB1506802).