An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network

Author:

Pattanaik Debasish1,Mishra Sanhita1,Khuntia Ganesh Prasad1,Dash Ritesh2,Swain Sarat Chandra1

Affiliation:

1. School of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, OdishaIndia

2. Department of Electrical Engineering, CCET, BhilaiIndia

Abstract

AbstractAnalysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e. to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517N, 85.0985E), India. were used in the model for forecasting performance of solar photovoltaic system.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering,Environmental Engineering

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