An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL)

Cuneyt Suheyl Ozveren, A. T. Sapeluk, Alan Birch

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

1 Citation (Scopus)

Abstract

Different ANN architectures using back propagation can forecast the electricity demand at half-hourly intervals for up to 24 hours ahead with various degrees of success that is highly dependent on mainly trial and error heuristic tailoring of the architecture and the various learning parameters to cover the solution space. This paper presents the results of an investigation of an approach in the neuro-evolution technique to the short term electricity forecasting (STFL) problem. This algorithm is called Neuro-Evolution through Augmenting Topologies (NEAT). We have chosen the methodology in the paper to be a simple, generic, adaptive, robust, and easy to implement approach, requiring modest computing resources, for the prediction of the electricity demand.
Original languageEnglish
Title of host publicationProceedings of 2014 49th International Universities Power Engineering Conference (UPEC)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781479965571, 9781479965564
DOIs
Publication statusPublished - 2014
Event49th International Universities Power Engineering Conference - Cluj-Napoca, Romania
Duration: 2 Sep 20145 Sep 2014
Conference number: 49th

Conference

Conference49th International Universities Power Engineering Conference
Abbreviated titleUPEC 2014
CountryRomania
CityCluj-Napoca
Period2/09/145/09/14

Fingerprint

Electricity
Topology
Backpropagation

Cite this

Ozveren, C. S., Sapeluk, A. T., & Birch, A. (2014). An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL). In Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC) (pp. 1-5). Piscataway, NJ: IEEE . https://doi.org/10.1109/UPEC.2014.6934819
Ozveren, Cuneyt Suheyl ; Sapeluk, A. T. ; Birch, Alan. / An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL). Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC). Piscataway, NJ : IEEE , 2014. pp. 1-5
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abstract = "Different ANN architectures using back propagation can forecast the electricity demand at half-hourly intervals for up to 24 hours ahead with various degrees of success that is highly dependent on mainly trial and error heuristic tailoring of the architecture and the various learning parameters to cover the solution space. This paper presents the results of an investigation of an approach in the neuro-evolution technique to the short term electricity forecasting (STFL) problem. This algorithm is called Neuro-Evolution through Augmenting Topologies (NEAT). We have chosen the methodology in the paper to be a simple, generic, adaptive, robust, and easy to implement approach, requiring modest computing resources, for the prediction of the electricity demand.",
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Ozveren, CS, Sapeluk, AT & Birch, A 2014, An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL). in Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC). IEEE , Piscataway, NJ, pp. 1-5, 49th International Universities Power Engineering Conference, Cluj-Napoca, Romania, 2/09/14. https://doi.org/10.1109/UPEC.2014.6934819

An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL). / Ozveren, Cuneyt Suheyl; Sapeluk, A. T.; Birch, Alan.

Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC). Piscataway, NJ : IEEE , 2014. p. 1-5.

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

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AB - Different ANN architectures using back propagation can forecast the electricity demand at half-hourly intervals for up to 24 hours ahead with various degrees of success that is highly dependent on mainly trial and error heuristic tailoring of the architecture and the various learning parameters to cover the solution space. This paper presents the results of an investigation of an approach in the neuro-evolution technique to the short term electricity forecasting (STFL) problem. This algorithm is called Neuro-Evolution through Augmenting Topologies (NEAT). We have chosen the methodology in the paper to be a simple, generic, adaptive, robust, and easy to implement approach, requiring modest computing resources, for the prediction of the electricity demand.

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Ozveren CS, Sapeluk AT, Birch A. An investigation into using neuro-evolution of Augmenting Topologies (NEAT) for short term load forecasting (STFL). In Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC). Piscataway, NJ: IEEE . 2014. p. 1-5 https://doi.org/10.1109/UPEC.2014.6934819