E3S Web Conf.
Volume 69, 2018International Conference Green Energy and Smart Grids (GESG 2018)
|Number of page(s)||6|
|Section||Properties, Regimes and Development of Renewable Energy Sources|
|Published online||27 November 2018|
Solar Power Prediction via Support Vector Machine and Random Forest
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan
* Corresponding author: email@example.com
Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (RF).
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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