基于深度学习的电气自动化设备能效预测与优化研究 |
| 郭鹏枭 |
| 北京普惠职道科技发展有限责任公司上海分公司，上海，200120 |
摘要：本文聚焦于基于深度学习的电气自动化设备能效预测与优化研究。首先阐述了研究背景与意义，指出电气自动化设备能效提升对能源节约和可持续发展的重要性，以及深度学习在能效预测与优化中的潜力。接着介绍了深度学习相关理论，包括常见模型及其特点。详细探讨了能效预测方法，涵盖数据采集与预处理、模型构建与训练、模型评估与优化等环节。深入研究了能效优化策略，包括基于预测结果的优化决策、优化算法的选择与应用、优化效果的评估与反馈。通过实际案例分析，验证了深度学习在电气自动化设备能效预测与优化中的有效性和可行性。最后总结研究成果，并对未来研究方向进行了展望。
关健词：深度学习；电气自动化设备；能效预测；能效优化
|
|
Research on Energy Efficiency Prediction and Optimization of Electrical Automation Equipment Based on Deep Learning |
Pengxiao Guo
Beijing Puhui Zhidao Technology Development Co., Ltd. Shanghai Branch, Shanghai, 200120, China
|
Abstract: This article focuses on the research of energy efficiency prediction and optimization of electricalautomation equipment based on deep learning. Firstly, the research background and significance wereelaborated, highlighting the importance of improving energy efficiency in electrical automation equipment forenergy conservation and sustainable development, as well as the potential of deep learning in energyefficiency prediction and optimization. Then, the relevant theories of deep learning were introduced, includingcommon models and their characteristics. Detailed discussions were conducted on energy efficiencyprediction methods, covering data collection and preprocessing, model construction and training, modelevaluation and optimization, and other aspects. We conducted in-depth research on energy efficiencyoptimization strategies, including optimization decisions based on predicted results, selection and applicationof optimization algorithms, evaluation and feedback of optimization effects. Through practical case analysis,the effectiveness and feasibility of deep learning in energy efficiency prediction and optimization of electricalautomation equipment have been verified. Finally, the research results were summarized and future researchdirections were discussed.
Keywords : deep learning; Electrical automation equipment; Energy efficiency prediction; energy efficiency optimization
参考文献
[1] 李明,张伟,王芳.基于LSTM-Transformer 混合模型的工业设备能效预测方法研究[J].自动化学报,2023,49(06):1234-1245.
[2] Chen H, Liu X, Li S. Multi-modal Data Fusion forBuilding Energy Consumption Prediction Using DeepLearning[J]. IEEE Transactions on IndustrialInformatics, 2022, 18(12): 8976-8987.
[3] 王强,赵磊,刘洋.基于深度强化学习的电气自动化系统动态优化控制[J].控制理论与应用,2023,40(03):456-467.
[4] Zhou Y, Wang J, Li X. Federated Learning forCross-factory Energy Efficiency Optimization inIndustrial Internet of Things[J]. IEEE Internet ofThings Journal, 2022, 9(15): 13456-13468.
[5] 陈静,杨帆,吴昊.数字孪生驱动的电气自动化设备能效动态评估方法[J].计算机集成制造系统,2023,29(05):1456-1468.
[6] Gupta A, Singh V, Kumar P. Edge Computing-Enabled Real-time Energy Optimization for SmartGrid Systems[J]. Renewable and Sustainable EnergyReviews, 2022, 167: 112734.
|
|
|
论文收录证明 / 文献检索报告
Document Retrieval Certificate / Proof of Publication Indexing
作者贡献声明 / 贡献确认书
Author Contribution Statement / Certificate of Authorship Contribution
同行评审报告 / 评审意见
Peer Review Report / Peer Review Comments
利益冲突
Conflict of Interest
作者声明不存在任何利益冲突。
The author declares no conflict of interest.
|
|
