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 language | English |
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Title of host publication | Proceedings of 2014 49th International Universities Power Engineering Conference (UPEC) |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781479965571, 9781479965564 |
DOIs | |
Publication status | Published - 2014 |
Event | 49th International Universities Power Engineering Conference - Cluj-Napoca, Romania Duration: 2 Sep 2014 → 5 Sep 2014 Conference number: 49th |
Conference
Conference | 49th International Universities Power Engineering Conference |
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Abbreviated title | UPEC 2014 |
Country/Territory | Romania |
City | Cluj-Napoca |
Period | 2/09/14 → 5/09/14 |
Keywords
- Electric load forecasting
- STFL
- Power systems
- Forecasting
- ANN
- Artificial neural networks
- Neuro-evolution
- Python
- NEAT
- Neuro-evolution of augmenting topologies