Predicting short-term energy usage in a smart home using hybrid deep learning models

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-09-05 DOI:10.3389/fenrg.2024.1323357
Imane Hammou Ou Ali, Ali Agga, Mohammed Ouassaid, Mohamed Maaroufi, Ali Elrashidi, Hossam Kotb
{"title":"Predicting short-term energy usage in a smart home using hybrid deep learning models","authors":"Imane Hammou Ou Ali, Ali Agga, Mohammed Ouassaid, Mohamed Maaroufi, Ali Elrashidi, Hossam Kotb","doi":"10.3389/fenrg.2024.1323357","DOIUrl":null,"url":null,"abstract":"The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models—LSTM and CNN—are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively.","PeriodicalId":12428,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Energy Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3389/fenrg.2024.1323357","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models—LSTM and CNN—are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用混合深度学习模型预测智能家居的短期能源使用情况
家庭能源消耗预测是智能电网(SG)中人工智能(AI)增强型能源管理领域的一个重要而又具有挑战性的课题。本研究的主要目标是为智能家居提供准确的能耗预测。本研究采用了两种深度学习模型:ConvLSTM 结合了卷积操作和长短期记忆(LSTM),而 CNN-LSTM 模型则协同了卷积神经网络(CNN)和 LSTM 网络。这两种混合模型都为空间和时间模式中的复杂关系建模提供了一种全面的方法。此外,还采用了 LSTM 和 CNN 两种基线模型进行比较分析。利用德克萨斯州休斯顿一个智能家居的真实数据,结果表明这两种混合模型都能提供高度准确的能耗预测。不过,ConvLSTM 模型的表现优于所有建议的模型,提前 1 天、3 天和 6 天预测的平均绝对百分比误差分别提高了 4.52%、9.59% 和 10.53%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
期刊最新文献
Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems Research on the impact of digitalization on energy companies’ green transition: new insights from China Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms Demand-side management scenario analysis for the energy-efficient future of Pakistan: Bridging the gap between market interests and national priorities Modeling and scheduling of utility-scale energy storage toward high-share renewable coordination
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1