Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability

IF 13 Q1 ENERGY & FUELS
Zhengyi Luo , Jinqing Peng , Xuefen Zhang , Haihao Jiang , Rongxin Yin , Yutong Tan , Mengxin Lv
{"title":"Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability","authors":"Zhengyi Luo ,&nbsp;Jinqing Peng ,&nbsp;Xuefen Zhang ,&nbsp;Haihao Jiang ,&nbsp;Rongxin Yin ,&nbsp;Yutong Tan ,&nbsp;Mengxin Lv","doi":"10.1016/j.adapen.2024.100182","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO<sub>2</sub> emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"15 ","pages":"Article 100182"},"PeriodicalIF":13.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000209/pdfft?md5=1cedc846099a911397ba821e7c77e286&pid=1-s2.0-S2666792424000209-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666792424000209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

Abstract Image

智能家居能源系统的优化调度:具有自学习能力的用户友好型自适应家庭智能代理
本文针对智能家居能源系统的优化调度,提出了一种具有自学习能力的用户友好型自适应家庭智能代理。该智能代理可根据系统运行数据自主确定模型参数,无需人工输入,因而更加方便实用。它还能从更新的数据集中自我学习最新的能源消耗信息,并自适应地调整模型参数,以适应不断变化的条件。利用这些确定的模型作为输入,智能代理使用所提出的多目标整数非线性优化模型执行日前优化调度,并自动控制系统运行。在实验室智能家居能源系统上进行了实验研究,以验证所开发的智能代理在不同场景下的有效性。结果表明,在所有三个场景中,平均绝对百分比误差均低于-12.7%,这表明了智能代理的准确性。此外,优化调度大大提高了系统性能。优化后,日常运营成本、峰谷差和二氧化碳排放量分别降低了 34.1% 至 81.6%、29.2% 至 36.7%、19.6% 至 43.2%。此外,光伏发电的自消耗率和自给率分别提高了 29.6% 至 38.0%,以及 40.5% 至 49.4%。所提出的智能代理为现实世界中智能家居能源系统的优化调度提供了宝贵的指导。
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 求助全文
来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信