基于TD3可变长度时间窗口最优加权的短期负荷预测策略  

Short-term load optimal weighted forecasting strategy based on TD3 variable length time window

作　　者：李志军[1] 徐博 张家安[1] 杨金荣 郭燕龙 LI Zhijun;XU Bo;ZHANG Jiaan;YANG Jinrong;GUO Yanlong(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;School of Artificial Intelligence,Hebei University of Technology,Tianjin 300130,China;School of Electrical Engineering,Hebei University of Technology,Tianjin 300130,China)

机构地区：[1]河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津市300130 [2]河北工业大学人工智能与数据科学学院,天津市300130 [3]河北工业大学电气工程学院,天津市300130

出　　处：《电力建设》2024年第6期140-148,共9页Electric Power Construction

基　　金：河北省科技支撑计划资助项目(15212105D)。

摘　　要：传统电力负荷组合模型使用滚动且固定长度时间窗口内的历史预测误差数据进行子模型变权,但该窗口长度无法根据最新环境特点进行自适应调整,导致有效信息的丢失或过时信息的引入,从而降低了短期负荷预测的准确性。利用双延迟深度确定性策略梯度模型(twin delay deep deterministic policy gradient,TD3)构建智能体,设计了一种时间窗口长度自适应可变的变权组合预测策略。通过建立短期负荷预测场景误差最低的目标及相关约束,设计了智能体的输入状态、动作和奖励机制,使智能体能够快速收敛并做出最优决策,从而准确地调整时间窗口长度。在此基础上,组合模型响应智能体实时指导的时间窗口,使用最优加权法实现了子模型的准确变权组合。最后,采用中国北方某地区的真实电力负荷数据进行算例分析,验证了所提策略的有效性和优越性。The traditional power load combination model uses the historical data prediction error in a rolling and fixedlength time window to perform sub-model variable weights.However,the window length cannot be adaptively adjusted according to the latest environmental characteristics,resulting in the loss of effective information or the introduction of outdated information,thereby reducing the accuracy of short-term load forecasting.In this study,a twin-delay deep deterministic policy gradient(TD3)was used to construct the agent,and a variable weight combination forecasting strategy with an adaptive variable time window length was designed.By establishing the target and related constraints with the lowest error in a short-term load forecasting scenario,the input state,action,and reward mechanism of the agent are designed such that the agent can quickly converge and make an optimal decision to accurately adjust the length of the time window.Consequently,the combination model responds to the time window guided by the agent in real time,and the optimal weighting method is used to realize an accurate variable weight combination of the submodels.Finally,real power load data from a certain area in northern China were used to verify the effectiveness and superiority of the proposed strategy.

关 键 词：短期负荷预测 可变长度时间窗口 组合模型 变权 深度强化学习 

分 类 号：TM714[电气工程—电力系统及自动化]