Solar power prediction in real time utilising supervised machine learning algorithms considering Madhya Pradesh region Online publication date: Tue, 05-Jul-2022
by Sanjiv Kumar Jain; Kaustubh Yawalkar; Prakhar Singh; Advait Apte
International Journal of Engineering Systems Modelling and Simulation (IJESMS), Vol. 13, No. 3, 2022
Abstract: Solar energy holds the key for future electric power generation providing sustainable development to meet the requirement for long lasting demand in the future. As exhaustible sources are depleting constantly, we need to focus on non-exhaustible sources. The prediction of solar power output is critical to plan for the future operations and integrating the power grid with renewable sources. In this study, a dataset of climatic parameters that is, beam (direct) irradiance, diffuse irradiance, reflected irradiance, sun height, air temperature and wind speed for five years is used to predict the solar power output based on photovoltaic technology of Indore region (27.2046°N, 77.4977°E). The performance of the trained models is determined using statistical (mathematical) indicators. Amongst the machine learning algorithms, the best accuracy of 98% is achieved by random forest method for the prediction of solar power output for Indore region.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Engineering Systems Modelling and Simulation (IJESMS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook