Heuristically enhanced dynamic neural networks for structurally improving photovoltaic power forecasting

Naji Al-Messabi, Cindy Goh, Ibrahim El-Amin, Yun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

6 Citations (Scopus)

Abstract

Among renewable generators, photovoltaic (PV) is showing an increasing suitability and a lowering cost. However, integration of renewable energy sources possesses many challenges, as the intermittency of these non-conventional sources often requires generation forecast, planning and optimal management. There exists scope to improve present PV yield forecasting models and methods. For example, the popular dynamic neural network modelling method suffers from the lack of a selection mechanism for an optimal network structure. This paper develops an enhanced network for short-term forecasting of PV power yield, termed a focused time-delay neural network (FTDNN). The problem of optimizing the FTDNN structure is reduced to optimizing the number of delay steps and the number of neurons in the hidden layer alone and this problem is conveniently solved through heuristics. Two such algorithms, a genetic algorithm and particle swarm optimization (PSO) have been tested and both prove efficient and can improve the forecasting accuracy of the dynamic network. Given the success of the PSO in solving this discontinuous structural optimization problem, it is expected that PSO offers potential in optimizing both the structure and parameters of a forecasting model.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages2820-2825
Number of pages6
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • photovoltaic power systems
  • power engineering computing
  • power generation planning
  • genetic algorithms,
  • load forecasting,
  • neural nets
  • particle swarm optimisation

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  • Cite this

    Al-Messabi, N., Goh, C., El-Amin, I., & Li, Y. (2014). Heuristically enhanced dynamic neural networks for structurally improving photovoltaic power forecasting. In Proceedings of the International Joint Conference on Neural Networks (pp. 2820-2825). [6889827] https://doi.org/10.1109/IJCNN.2014.6889827