Forecasting of photovoltaic power using regularized ensemble Extreme Learning Machine

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

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

6 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 publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
PublisherIEEE
Pages455-458
Number of pages4
ISBN (Electronic)978-1-5090-2597-8
ISBN (Print)978-1-5090-2598-5
DOIs
Publication statusPublished - 9 Feb 2017

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON

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