[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84750-en":3,"doc-seo-84750-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84750,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","LLMs for Agentic Home Energy Management","Home Energy Management Systems (HEMS) can lower residential electricity bills and enable demand response, yet adoption is constrained by the challenge of converting household preferences into technical scheduling constraints. This paper tests whether large language model (LLM) agents provide a practical natural-language interface for multi-appliance home energy scheduling. A tool-calling ReAct agent uses live Octopus Agile prices, weather forecasts, photovoltaic estimates, household usage data, and a retrieval-augmented knowledge base, benchmarked against MILP ground truth across tariff days, conflicts, solar co-optimization, and week-long deployment.","LLMs for Agentic Home Energy Management  \nSokipriala Jonah  \nCentre for Computational Science and Mathematical Modelling  \nCoventry University  \nCoventry, United Kingdom  \narXiv :2607 .04569v1 [ ee ss . SY] 6 Jul 2026  \nAbstract—Home Energy Management Systems (HEMS) can reduce residential electricity costs and support demand response, but adoption is limited by the difficulty of translating household preferences into technical scheduling constraints. This paper evaluates whether large language model (LLM) agents can provide a practical natural-language interface for multiappliance home energy scheduling. We present a tool-calling ReAct agent that uses live half-hourly Octopus Agile prices, weather forecasts, photovoltaic generation estimates, household usage data, and a retrieval-augmented knowledge base to schedule flexible loads against a mixed-integer linear programming (MILP) ground truth. Three commercial models, GPT-4o-mini, Gemini 2.5 Flash, and Claude Sonnet 4.6, are benchmarked across tariff days, constraint-conflict scenarios, weather-aware solar co-optimization, and week-long deployment. With native function calling, all models achieve 100% scheduling success and near-MILP optimality, while text-parsed action interfaces sharply reduce reliability. Constraint testing shows that cost-optimal and safety-optimal models differ: Claude is strongest under infeasibility and power-cap conflicts, while GPT-4o-mini is most efficient. Over a simulated week, agents capture 96.7–98.0% of oracle savings, projecting approximately £1,270 annual savings over an off-peak timer baseline. Code and a live demonstration are available at [https://github.com/sokistar24/ecohome-energy-agent](https://github.com/sokistar24/ecohome-energy-agent)  \nand [https://www.ecohomeagent.com/](https://www.ecohomeagent.com/).  \nIndex Terms—Agentic AI, home energy management systems, large language models, demand response, load scheduling, solar self-consumption, dynamic retail tariffs, LLM benchmarking.  \nI. INTRODUCTION  \nTHE decarbonization of electricity systems requires rising  \nshares of variable renewable generation to be integrated while maintaining reliable and affordable supply. This creates a growing need for demand-side flexibility [1], [2] . The International Energy Agency projects that global demandresponse capacity must grow roughly tenfold between 2020 and 2030, with buildings and residential electric vehicles (EVs) contributing much of this potential [3], [4], [5] . The residential sector is especially attractive because flexible loads, including EV charging, wet appliances, heating, and cooling, can often be shifted in time without materially affecting the service delivered to occupants [6], [7] .  \nHome Energy Management Systems (HEMS) are intended to realize this flexibility by scheduling household devices against price and generation signals. Although HEMS have demonstrated cost reductions and demand-response value, deployment remains below the level required for decarbonization targets [3], [8] . A key barrier is the user-interaction burden: conventional HEMS require households to translate everyday preferences into technical parameters, which discourages adoption among non-expert users [9], [10] .  \nLarge language models (LLMs) offer a possible route around this barrier. Modern LLMs can follow task instructions without example demonstrations [11], [12], [13], invoke external tools, and, when organized as agents that interleave reasoning with actions [14], [15], decompose a natural language request into data retrieval, analysis, and control decisions. Beyond reducing the parameterization burden, a conversational agent can explain its recommendations, answer ad hoc questions about consumption and tariffs, and ground its advice ina curated knowledge base of energy-saving practice through retrieval-augmented generation (RAG) .  \nA small but growing body of work has begun to apply LLMs to home energy management, including conversational en","cbCaid5gQDOeEMSl","https://ap.wps.com/l/cbCaid5gQDOeEMSl","pdf",1753142,2,1,17,"English","en",105,"# Introduction\n## Demand-side flexibility and the role of HEMS\n## User-interaction barrier in conventional HEMS\n## LLMs as conversational and agentic interfaces\n## Related work and the motivation for open questions\n## Paper focus: model coverage, PV-aware optimization, and robustness","[{\"question\":\"What problem does the paper address in home energy management adoption?\",\"answer\":\"HEMS adoption is limited because users must translate everyday preferences into technical scheduling constraints, which discourages non-expert households.\"},{\"question\":\"How does the proposed agentic LLM system schedule home energy loads?\",\"answer\":\"It uses a tool-calling ReAct agent with live price data, weather forecasts, photovoltaic generation estimates, household usage data, and a retrieval-augmented knowledge base to produce schedules matched against MILP ground truth.\"},{\"question\":\"Which benchmarks and scenarios are used to evaluate the LLM agents?\",\"answer\":\"Evaluations include tariff-day testing, constraint-conflict scenarios, weather-aware solar co-optimization, and a week-long deployment simulation, with comparisons against MILP optimality.\"}]",1784198032,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"llms-for-agentic-home-energy-management","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/llms-for-agentic-home-energy-management/84750/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-21","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in home energy management adoption?","Question",{"text":75,"@type":76},"HEMS adoption is limited because users must translate everyday preferences into technical scheduling constraints, which discourages non-expert households.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed agentic LLM system schedule home energy loads?",{"text":80,"@type":76},"It uses a tool-calling ReAct agent with live price data, weather forecasts, photovoltaic generation estimates, household usage data, and a retrieval-augmented knowledge base to produce schedules matched against MILP ground truth.",{"name":82,"@type":73,"acceptedAnswer":83},"Which benchmarks and scenarios are used to evaluate the LLM agents?",{"text":84,"@type":76},"Evaluations include tariff-day testing, constraint-conflict scenarios, weather-aware solar co-optimization, and a week-long deployment simulation, with comparisons against MILP 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