信息与控制2024，Vol.53Issue(5)：561-573,13.DOI:10.13976/j.cnki.xk.2024.3168
基于强化学习的电池制造能力可变权组合预测
Variable Weight Combination Forecasting of Battery Manufacturing Capacity Based on Reinforcement Learning
摘要
Abstract
For the nonlinear and nonstationary characteristics of lithium battery manufacturing capacity data and the difficulty in ensuring the error stability of long-term time series because existing time-series prediction methods typically fall into local optimum,we propose a variable weight combina-tion forcasting model for battery manufacturing capacity based on Q-learning and combination forca-sting methods.First,to deal with the complex characteristics of battery manufacturing capacity da-ta,we use a long short-term memory neural network,a gated recurrent unit neural network,and a seasonal differential autoregressive sliding average model to learn and predict historical data.Sec-ond,we introduce the sliding window algorithm to divide the complete time series into short se-quences,which is more conducive to mining data features,and design Q-learning combined with entropy to obtain the optimal sliding window length to ensure that the overall prediction error under each window is more stable.Finally,to fully utilize the prediction effects of each single prediction algorithm at different times,we design a two-layer reinforcement learning strategy to perform time-varying weighting for different single prediction results under each window,thereby achieving the optimal time-varying weight combination.Engineering example analysis shows that the proposed triple reinforcement learning variable weight combination forcasting algorithm can effectively im-prove the prediction accuracy of lithium battery manufacturing capacity.关键词
强化学习/组合预测/时变权重/电池Key words
reinforcement learning/combination forecasting/time-varying weight/battery分类
信息技术与安全科学引用本文复制引用
俞银泉,王子赟,王艳..基于强化学习的电池制造能力可变权组合预测[J].信息与控制,2024,53(5):561-573,13.基金项目
国家重点研发计划(2020YFB1710600) （2020YFB1710600）
江苏省自然科学基金面上项目(BK20221533) （BK20221533）
江苏省科协青年科技人才托举工程项目(TJ-2021-006) （TJ-2021-006）
