Smart City Gnosys

Smart city article details

Title	Machine Learning In Renewable Energy Systems For Smart Cities
ID_Doc	35996
Authors	Haque A.; Malik A.
Year	2024
Published	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things
DOI	http://dx.doi.org/10.1201/9781032669809-13
Abstract	Machine learning exerts a crucial role in the area of renewable energy with respect to smart cities. Machine learning methods are employed within smart grid systems to improve energy efficiency, forecast load demand, detect energy theft, and manage demand-side operations. Through the analysis of real-time data collected from smart meters, sensors, and Internet of Things (IoT) devices, machine learning algorithms offer valuable insights for optimizing the grid, scheduling loads, and promoting energy conservation. Machine learning algorithms contribute to optimizing the operation and management of power grids that heavily rely on renewable energy sources. Through the analysis of historical energy data encompassing solar irradiance, wind speed, and electricity demand, machine learning algorithms develop precise energy forecasting models. These models facilitate improved planning and management of renewable energy systems by predicting future energy production or demand, optimizing grid integration, and facilitating efficient resource allocation. Additionally, these techniques prove valuable in anomaly detection and fault diagnosis in renewable energy systems, such as solar panels or wind turbines. By analyzing geographical, meteorological, and satellite data, machine learning algorithms estimate the solar or wind energy potential of specific regions, assisting in site selection for renewable energy projects. Overall, this chapter demonstrates that the application of machine learning in smart cities will help enhance the efficiency, reliability, and seamless integration of renewable energy sources into the existing energy infrastructure. © 2024 selection and editorial matter, Binbing Yu and Kristine Broglio.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
35876	0.932	Tejaswi P.; Gnana Swathika O.V.	Machine Learning Algorithms-Based Solar Power Forecasting In Smart Cities	Artificial Intelligence and Machine Learning in Smart City Planning (2023)
35986	0.923	Ahmed S.R.; Hussain A.-S.T.; Majeed D.A.; Jghef Y.S.; Tawfeq J.F.; Taha T.A.; Sekhar R.; Solke N.; Ahmed O.K.	Machine Learning For Sustainable Power Systems: Aiot-Optimized Smart-Grid Inverter Systems With Solar Photovoltaics	Lecture Notes in Networks and Systems, 1036 LNNS (2024)
36026	0.921	Gaamouche R.; Chinnici M.; Lahby M.; Abakarim Y.; Hasnaoui A.E.	Machine Learning Techniques For Renewable Energy Forecasting: A Comprehensive Review	Green Energy and Technology (2022)
52237	0.915	Almadhor A.; Mallikarjuna K.; Rahul R.; Chandra Shekara G.; Bhatia R.; Shishah W.; Mohanavel V.; Suresh Kumar S.; Thimothy S.P.	Solar Power Generation In Smart Cities Using An Integrated Machine Learning And Statistical Analysis Methods	International Journal of Photoenergy, 2022 (2022)
36056	0.903	Tiwari S.; Jain A.; Ahmed N.M.O.S.; Charu; Alkwai L.M.; Dafhalla A.K.Y.; Hamad S.A.S.	Machine Learning-Based Model For Prediction Of Power Consumption In Smart Grid- Smart Way Towards Smart City	Expert Systems, 39, 5 (2022)
36081	0.902	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)
22431	0.902	Rajaan R.; Baishya B.K.; Rao T.V.; Pattanaik B.; Tripathi M.A.; Anitha R.	Efficient Usage Of Energy Infrastructure In Smart City Using Machine Learning	EAI Endorsed Transactions on Internet of Things, 10 (2024)
23799	0.902	Indra G.; Gopi T.; Gangwar P.K.; Senthil Kumar K.R.; Rajendiran M.; Balasubramani S.	Enhancing Energy Efficiency In Smart Cities Through Neural Support Vector Machine Learning For Smart Grids	Communications in Computer and Information Science, 1971 CCIS (2025)
36015	0.901	Suanpang P.; Jamjuntr P.	Machine Learning Models For Solar Power Generation Forecasting In Microgrid Application Implications For Smart Cities	Sustainability (Switzerland), 16, 14 (2024)
50834	0.9	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)