Forecasting of photovoltaic power using extreme learning machine

T. T. Teo, T. Logenthiran, W. L. Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

64 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015
PublisherIEEE
ISBN (Electronic)9781509012381
DOIs
Publication statusPublished - 21 Jan 2016
EventIEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015 - Bangkok, Thailand
Duration: 3 Nov 20156 Nov 2015

Conference

ConferenceIEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2015
CountryThailand
CityBangkok
Period3/11/156/11/15

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