南方电网技术2024，Vol.18Issue(11)：79-87,9.DOI:10.13648/j.cnki.issn1674-0629.2024.11.009
考虑多变量建模的中期负荷预测模型
Medium-Term Load Forecasting Model Considering Multivariate Modeling
摘要
Abstract
Medium-term load forecasting is influenced by multiple external variables such as temperature,holidays,and weekends.Although long short-term memory(LSTM)networks have shown strong predictive ability in short-term load forecasting,they cannot establish a good correlation between multiple external variables and predicted load in medium-term load forecasting.To address the above issues,parallel LSTM structures and time series N-node tree LSTM(t-N Tree LSTM)structures are proposed.By introducing branch structures and tree structures to construct finer feature granularity,modeling of medium-term load forecasting is achieved.Finally,experiments are conducted on the 2017 global energy forecasting competition dataset GEFCom2017,and the results show that finer feature granularity is beneficial for obtaining higher accuracy prediction results in the medium-term load forecasting process,verifying the effectiveness of the parallel LSTM model and t-N Tree LSTMs model.关键词
中期负荷预测/长短时记忆网络/时间序列/特征粒度/多变量建模Key words
medium-term load forecasting/LSTM/time series/feature granularity分类
动力与电气工程引用本文复制引用
徐利美,赵金,李裕民,姚非,邢吉伟,续欣莹..考虑多变量建模的中期负荷预测模型[J].南方电网技术,2024,18(11):79-87,9.基金项目
国家重点研发计划资助项目(2017YFA0700304) （2017YFA0700304）
山西省自然科学基金面上项目(20210302123189). Supported by the National Key Research and Development Program of China(2017YFA0700304) （20210302123189）
the Natural Science Foundation of Shanxi Province(20210302123189). （20210302123189）
