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Title	Machine Learning Algorithms-Based Solar Power Forecasting In Smart Cities
ID_Doc	35876
Authors	Tejaswi P.; Gnana Swathika O.V.
Year	2023
Published	Artificial Intelligence and Machine Learning in Smart City Planning
DOI	http://dx.doi.org/10.1016/B978-0-323-99503-0.00001-6
Abstract	Renewable energy resources are the best solution to meet the growing power requirement of smart cities. Solar energy is one of the inexhaustible, clean, and economically viable green energy resources. Nowadays, photovoltaic systems are playing a major role in meeting the energy demands of the future. The uncertain climatic conditions and exhaustion of fossil fuel systems lead to vast use of photovoltaic systems worldwide. The integration of solar power with power grids in the actual power system causes anomalies such as abrupt uncertainties, climate changes causing system instability, voltage surges, inefficient utility planning, and economical loss. The forecasting of renewable energy is difficult for dispatch of generation and optimal balancing in smart grid due to high fluctuations. The confidence and accuracy level of photovoltaic forecasting are a challenge due to computational cost. Therefore, forecasting techniques play a crucial step for accurate forecasting of PV output and successful integration of PV into the grid. The random forest regression and artificial neural network machine learning algorithms are best suited for accurate prediction of solar power output. The performance of these machine learning techniques is compared in terms of correlation coefficient and performance metrics. The forecasting accuracy of PV output is measured by different performance indices. © 2023 Elsevier Inc. All rights reserved.
Author Keywords	Artificial neural network; Machine learning; Random forest; Solar power forecasting; Test data; Train data

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