电网技术2026，Vol.50Issue(6)：2278-2291,中插8-中插9,16.DOI:10.13335/j.1000-3673.pst.2025.1125
考虑电力中长期交易机制的次月典型日负荷曲线预测
Typical Daily Load Curve Forecasting for the Next Month Under Medium-and Long-term Electricity Trading Mechanisms
摘要
Abstract
With the rapid development of the national unified electricity market,the accurate prediction of typical curves has become a key issue for the effective connection and interaction between medium-and long-term markets and spot markets.This paper focuses on predicting the typical daily load curve for the next month and proposes a user-group typical daily load prediction framework that integrates time-of-use electricity prices and multi-scale time-series features.First,a Mamba-LWT(Mamba-Local Window Transformer)dual-channel time-series model is constructed to capture seasonal macro-trends and intra-day local fluctuations,respectively.Through adaptive aggregation in long-short time-series routing,the model accurately models the multi-scale characteristics of load curves.Meanwhile,an improved transfer learning strategy is innovatively introduced into the model.It pre-trains general time-series features using source-domain data and then supplements time-of-use electricity price features via hierarchical fine-tuning with target-domain data,effectively addressing the problem of insufficient target-domain samples under the current time-of-use electricity price policy.Finally,the effectiveness of the proposed model is verified using electricity consumption data from industrial and commercial users in a city in northern China.The proposed framework outperforms the selected mainstream comparison models in point prediction,with the errors for all prediction indicators reduced by more than 10.32％.In interval prediction,all indicators of the 90％confidence interval are improved by at least 2.21％relative to existing models.This study will provide a solid data foundation for subsequent electricity trading strategies and also serve as an important reference for the sustainable development of the electricity market.关键词
次月典型日负荷预测/Mamba-LWT双通道时序模型/改进迁移学习/分时段电价/区间预测Key words
load forecasting of typical day in the next month/Mamba-LWT dual-channel time-series model/hierarchical transfer learning/time segment price/interval forecasting分类
信息技术与安全科学引用本文复制引用
边文钰,史佳琪,刘念,丁一,高齐,李欣芝..考虑电力中长期交易机制的次月典型日负荷曲线预测[J].电网技术,2026,50(6):2278-2291,中插8-中插9,16.基金项目
国家自然科学基金项目(52407128).Project Supported by National Natural Science Foundation of China(52407128). （52407128）
