Multi-horizon Short-Term Load Consumption Forecasting Using Deep Learning Models
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-35245-4_26
Reference14 articles.
1. Liu, T., Chengliang, X., Chen, H., Li, Z.: Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. (2019). https://doi.org/10.1016/j.enbuild.2019.109675
2. Syed, D., Abu-Rub, H., Ghrayeb, A., Refaat, S.S.: Household-level energy forecasting in smart buildings using a novel hybrid deep learning model. IEEE Access 9, 33498–33511 (2021). https://doi.org/10.1109/ACCESS.2021.3061370
3. Jrhilifa, I., Ouadi, H., Jilbab, A.: Smart home's wireless sensor networks lifetime optimizing using Q-learning. In: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, pp. 1–6 (2021). https://doi.org/10.1109/IECON48115.2021.9589460
4. Hadri, S., Naitmalek, Y., Najib, M., Bakhouya, M., Fakhri, Y., Elaroussi, M.: A comparative study of predictive approaches for load forecasting in smart buildings. Procedia Comput. Sci., 160, 173–180 (2019). ISSN 1877–0509. https://doi.org/10.1016/j.procs.2019.09.458
5. Yazici, I., Faruk Beyca, O., Delen, D.: Deep-learning-based short-term electricity load forecasting: a real case application. Eng. Appl. Artif. Intell. 109 (2022). https://doi.org/10.1016/j.engappai.2021.104645
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code;Energy;2024-10
2. Forecasting smart home electricity consumption using VMD-Bi-GRU;Energy Efficiency;2024-04
3. Efficient real-time cost optimization of a two-layer electric water heater system under model uncertainties;Energy Conversion and Management;2024-03
4. Experimental Analysis of Efficient Dual-Layer Energy Management and Power Control in an AC Microgrid System;IEEE Access;2024
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3