Smart City Gnosys

Smart city article details

Title	An Optimized Demand For Cost And Environment Benefits Towards Smart Residentials Using Iot And Machine Learning
ID_Doc	8877
Authors	Hemlata; Rai M.
Year	2024
Published	Sustainable Smart Homes and Buildings with Internet of Things
DOI	http://dx.doi.org/10.1002/9781394231522.ch17
Abstract	Reducing greenhouse gas emissions requires smart buildings. Growing in popularity, machine learning (ML) may improve decarbonization management and analytics for smart buildings. It's an essential tool for many industries, including smart cities. Grid consumers are seeing the emergence of energy communities. Buildings can learn thanks to artificial intelligence (AI) and ML. Learn about the capabilities of ML algorithms in smart systems. In this chapter, we shall define machine-based learning algorithms and provide a general overview of smart-based systems. We will examine these algorithms’ advantages, difficulties, and practical uses. We will also go over important implementation concerns and future developments in this fascinating sector. © 2025 Scrivener Publishing LLC. All rights reserved.
Author Keywords	adaptive CC virtual machine resource allocation; cyber risk optimizer for smart homes; HEMS (homes energy management systems); optimal control framework (OCF); prediction- and feedback-based proactively energy conservation

Similar Articles

Id	Similarity	Authors	Title	Published
51061	0.92	Arabasy M.; Hussein M.F.; Abu Osba R.; Al Dweik S.	Smart Housing: Integrating Machine Learning In Sustainable Urban Planning, Interior Design, And Development	Asian Journal of Civil Engineering, 26, 1 (2025)
32444	0.912	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023)
5161	0.904	Masroor M.; Rezazadeh J.; Ayoade J.; Aliehyaei M.	A Survey Of Intelligent Building Automation With Machine Learning And Iot	Advances in Building Energy Research, 17, 3 (2023)
13024	0.903	Ardabili S.; Mosavi A.; Várkonyi-Kóczy A.R.	Building Energy Information: Demand And Consumption Prediction With Machine Learning Models For Sustainable And Smart Cities	Lecture Notes in Networks and Systems, 101 (2020)
35892	0.894	Sen S.; Yadeo D.; Kumar P.; Kumar M.	Machine Learning And Predictive Control-Based Energy Management System For Smart Buildings	Artificial Intelligence and Machine Learning in Smart City Planning (2023)
36081	0.894	Prasad M.S.C.; Dhanalakshmi M.; Mohan M.; Somasundaram B.; Valarmathi R.; Boopathi S.	Machine Learning-Integrated Sustainable Engineering And Energy Systems: Innovations At The Nexus	Harnessing High-Performance Computing and AI for Environmental Sustainability (2024)
35929	0.892	Mohamed B.; Hussein A.I.	Machine Learning Based Energy Management System For Smart Buildings	2025 15th International Conference on Power, Energy, and Electrical Engineering, CPEEE 2025 (2025)
2539	0.89	Islam M.B.; Guerrieri A.; Gravina R.; Fortino G.	A Meta-Survey On Intelligent Energy-Efficient Buildings	Big Data and Cognitive Computing, 8, 8 (2024)
50834	0.887	Akram A.S.; Abbas S.; Khan M.A.; Athar A.; Ghazal T.M.; Al Hamadi H.	Smart Energy Management System Using Machine Learning	Computers, Materials and Continua, 78, 1 (2024)
35996	0.886	Haque A.; Malik A.	Machine Learning In Renewable Energy Systems For Smart Cities	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things (2024)