{"title":"Q-GRID SMART:一个基于区块链的智能家居能源管理和分析系统","authors":"Ameni Boumaiza","doi":"10.1016/j.rineng.2025.107093","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents Q-GRID SMART, a decentralized, blockchain-enabled residential energy management platform integrating IoT-based monitoring, predictive analytics, and an interactive user dashboard. In a one-month pilot across 4,196 households in Doha, Qatar, machine learning models (GRU, Bi-LSTM) forecasted energy consumption, cost, and CO<sub>2</sub> emissions with RMSE = 160.9 kWh and MAE = 120.3 kWh. Post-deployment surveys (n = 312) indicated a Net Promoter Score of +42 and 87% reported improved energy awareness. The platform achieved an average 16.8% electricity reduction and 145.4 kg CO<sub>2</sub> savings per household per month. We further analyze how blockchain latency and confirmation times affect real-time control and user experience, proposing mitigation via edge control loops, batching, and Layer-2 solutions (state channels, rollups). These results demonstrate Q-GRID SMART's potential to deliver scalable, secure, and user-centric energy management solutions for utilities and households.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"28 ","pages":"Article 107093"},"PeriodicalIF":7.9000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Q-GRID SMART: A blockchain-enabled smart home energy management and analytics system\",\"authors\":\"Ameni Boumaiza\",\"doi\":\"10.1016/j.rineng.2025.107093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study presents Q-GRID SMART, a decentralized, blockchain-enabled residential energy management platform integrating IoT-based monitoring, predictive analytics, and an interactive user dashboard. In a one-month pilot across 4,196 households in Doha, Qatar, machine learning models (GRU, Bi-LSTM) forecasted energy consumption, cost, and CO<sub>2</sub> emissions with RMSE = 160.9 kWh and MAE = 120.3 kWh. Post-deployment surveys (n = 312) indicated a Net Promoter Score of +42 and 87% reported improved energy awareness. The platform achieved an average 16.8% electricity reduction and 145.4 kg CO<sub>2</sub> savings per household per month. We further analyze how blockchain latency and confirmation times affect real-time control and user experience, proposing mitigation via edge control loops, batching, and Layer-2 solutions (state channels, rollups). These results demonstrate Q-GRID SMART's potential to deliver scalable, secure, and user-centric energy management solutions for utilities and households.</div></div>\",\"PeriodicalId\":36919,\"journal\":{\"name\":\"Results in Engineering\",\"volume\":\"28 \",\"pages\":\"Article 107093\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Results in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590123025031482\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590123025031482","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Q-GRID SMART: A blockchain-enabled smart home energy management and analytics system
This study presents Q-GRID SMART, a decentralized, blockchain-enabled residential energy management platform integrating IoT-based monitoring, predictive analytics, and an interactive user dashboard. In a one-month pilot across 4,196 households in Doha, Qatar, machine learning models (GRU, Bi-LSTM) forecasted energy consumption, cost, and CO2 emissions with RMSE = 160.9 kWh and MAE = 120.3 kWh. Post-deployment surveys (n = 312) indicated a Net Promoter Score of +42 and 87% reported improved energy awareness. The platform achieved an average 16.8% electricity reduction and 145.4 kg CO2 savings per household per month. We further analyze how blockchain latency and confirmation times affect real-time control and user experience, proposing mitigation via edge control loops, batching, and Layer-2 solutions (state channels, rollups). These results demonstrate Q-GRID SMART's potential to deliver scalable, secure, and user-centric energy management solutions for utilities and households.