Forecasting of Solar PV Output Power using Artificial Neural Network
M Vasudha1, Sudha Dukkipati2, S Hari Chandhan3
1M Vasudha, Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P), India.
2Sudha Dukkapati, Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P), India.
3S Hari Chandhan, Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram (A.P), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1225-1227 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6860048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rapid increase of variable renewable energy resources like PV and Wind has become difficult on the grid side because of their output variations. The PV generation is irregular that can’t carryout constant power. Through the prediction variety of the uncertainties are going to be reduced. the amount of irradiance and temperature received at a specific area is that the foremost important meteoric parameter for prediction of solar PV power. The irradiance and temperature information used for this analysis is collected from KLEF with a span of one year.In this paper an approach is proposed in which the PV output power is predicted using ANN.
Keywords: Solar PV, Forecasting, ANN, Predication
Scope of the Article: Artificial Intelligence and Machine Learning