沈阳工业大学学报2025，Vol.47Issue(3)：309-316,8.DOI:10.7688/j.issn.1000-1646.2025.03.06
基于多因素特征工程建模的电力负荷预测方法
Electric load forecasting method based on multi-factor feature engineering modeling
摘要
Abstract
[Objective]Accurate electric load forecasting is the key to the smooth operation and effective management of power systems,which can enable power companies to effectively dispatch power generation equipment,thereby improving their operational efficiency and economic benefits.However,electric load data are affected by a variety of external factors and have significant time dependence,which makes their accurate prediction difficult.Therefore,an electric load forecasting model combining multi-factor modeling and time series analysis was proposed,which taking into account the analysis of the complex influences of multiple factors and the time dependence characteristics of electric load,so as to realize accurate electric load forecasting.[Methods]To break through the respective limitations of multi-factor analysis methods and time series forecasting modeling methods,an improved electric load forecasting model combining long short-term memory(LSTM)network and Bayesian optimization algorithm was proposed with the help of deep learning and a multi-factor analysis method.Firstly,a comprehensive multi-factor feature pool was constructed,including the historical time series features of electric load and a variety of external factors to fully capture the complex relationships between electric load data and multiple influencing factors.Secondly,the LSTM network was used as the core model,and its unique gating mechanism and memory unit were used to capture the time dependence of electric load data and the complex association between multiple factors.The Bayesian optimization algorithm was introduced to tune the hyperparameters of the LSTM model,and the Gaussian process was used as the surrogate model to make full use of the prior information to improve the training efficiency and prediction performance of the model.[Results]Five real transformer datasets were used to train and test the model.The effectiveness of the model was verified by several evaluation indicators.The proposed electric load forecasting method based on multi-factor feature engineering modeling has significantly better prediction performance than the model using only a single factor for forecasting on five different transformer datasets,which further highlights the effectiveness of the multi-factor feature pool.The maximum coefficient of determination of the LSTM model is 0.920 7,and the minimum mean square error and the minimum mean absolute error are 0.042 and 0.024,respectively.The results demonstrate the superior performance of the proposed method in complex electric load forecasting tasks.[Conclusion]The electric load forecasting model combining multi-factor modeling and time series analysis fully considers the complexity of external factors and the time dependence characteristics of electric load and innovatively introduces a comprehensive feature pool to participate in LSTM model training and testing.The LSTM network combined with multi-factor feature pool modeling has high prediction accuracy and robustness,which provides a new technical idea for electric load forecasting,has important reference value for the planning and dispatch of smart grid,and lays a foundation for further development of accurate load forecasting technology.关键词
电力负荷预测/LSTM网络/贝叶斯优化/多因素分析/时间序列预测/特征工程/数据驱动建模/深度学习Key words
electric load forecasting/long short-term memory network/Bayesian optimization/multi-factor analysis/time series forecasting/feature engineering/data-driven modeling/deep learning分类
信息技术与安全科学引用本文复制引用
刘硕,丁宇昂,赵梓焱..基于多因素特征工程建模的电力负荷预测方法[J].沈阳工业大学学报,2025,47(3):309-316,8.基金项目
国家自然科学基金项目(62203093) （62203093）
辽宁省科技计划联合计划(基金)项目(2023-MSBA-074). （基金）
