Forecasting of photovoltaic power using extreme learning machine

Teo, Tiong Teck, Logenthiran, Thillainathan and Woo, Wai Lok (2015) Forecasting of photovoltaic power using extreme learning machine. In: 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA). IEEE. ISBN 978-1-5090-1238-1

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ISGT-Asia.2015.7387113

Abstract

This paper aims to forecast the photovoltaic power, which is an important and challenging function of energy management system for grid planning, scheduling, maintenance and improving stability. Forecasting of photovoltaic power using Artificial Neural Network (ANN) is the main focus of this paper. The training algorithm used for ANN is Extreme Learning Machine (ELM). Accurate forecast of Renewable Energy Sources (RES) is important for grid operators. It can help the grid operators to anticipate when there will be a shortage or surplus of RES and make the necessary generation planning. Therefore, a real and accurate data were used to train and test the developed ANN. In this paper, MATLAB is used to create and implement the neural network model. Simulation studies were carried out on the developed model and simulation results show that, the proposed neural network model forecasts the photovoltaic power with high accuracy.

Item Type: Book Section
Subjects: G900 Others in Mathematical and Computing Sciences
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 04 Apr 2019 14:37
Last Modified: 04 Apr 2019 14:37
URI: http://nrl.northumbria.ac.uk/id/eprint/38763

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