Forecasting of photovoltaic power using regularized ensemble Extreme Learning Machine

Teo, Tiong Teck, Logenthiran, Thillainathan, Woo, Wai Lok and Abidi, Khalid (2017) Forecasting of photovoltaic power using regularized ensemble Extreme Learning Machine. In: 2016 IEEE Region 10 Conference (TENCON). IEEE, pp. 455-458. ISBN 978-1-5090-2598-5

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The increasing penetration of renewable energy sources with intermittent nature generation challenges the grid operator to accurately plan and schedule their generators. In this context accurate forecasting model are vital to ensure smooth day-to-day operation with high renewable energy sources. Artificial Neural Network (ANN) have shown promising ability for accurate forecast. The ANN proposed in this paper are trained using historical dataset and training algorithm, Extreme Learning Machine (ELM). ELM requires randomly initialized parameters which affect the forecasting model. This paper propose a method to reduce the randomness of ELM by adding a regularizing term and combining multiple ELM. The ANN is implemented using MATLAB and trained using real-life data. The result shows that the randomness are greatly reduce and has a higher forecasting accuracy than a single ELM.

Item Type: Book Section
Uncontrolled Keywords: Forecasting, extreme learning machine, energy management system, renewable energy sources, photovoltaic, day-ahead, ensemble, regularized
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 29 Mar 2019 15:28
Last Modified: 10 Oct 2019 20:48

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