Publication Type:

Conference Paper

Source:

Procedia Computer Science, Elsevier B.V., Volume 115, p.723-730 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032436193&doi=10.1016%2fj.procs.2017.09.143&partnerID=40&md5=8d987152ecc6021577c9c3ac4b5028bd

Keywords:

Electric power transmission networks, Errors, Forecasting, MATLAB, Mean absolute error, Mean absolute percentage error, Photovoltaic, Photovoltaic cells, Regression analysis, Solar cell arrays, Solar concentrators, Solar energy, Solar irradiances, Solar power generation, Support vector regression (SVR), Thermoelectric power

Abstract:

<p>As the penetration of photovoltaic power is increasing, utilities are concerned about its impact on distribution grid. Due to the variable nature of solar power, predicting the power output of solar panel installation is important for its optimal use. This paper proposes a new method for forecasting the power output from a solar panel using multi input Support Vector Regression model. The performance has been analysed and compared with Analytical PV power forecasting model. Both the models are simulated and performance evaluation is done using MATLAB. Mean Absolute Percentage Error and Mean Absolute Error are used to assess forecasting models. © 2017 The Author(s).</p>

Notes:

cited By 0; Conference of 7th International Conference on Advances in Computing and Communications, ICACC 2017 ; Conference Date: 22 August 2017 Through 24 August 2017; Conference Code:131212

Cite this Research Publication

R. Nageem and Jayabarathi, R., “Predicting the Power Output of a Grid-Connected Solar Panel Using Multi-Input Support Vector Regression”, in Procedia Computer Science, 2017, vol. 115, pp. 723-730.

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