Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology
| Autor: | Mourad Mordjaoui, T.E. Boukelia, Farida Laouafi, Abderrezak Laouafi |
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| Rok vydání: | 2016 |
| Předmět: |
Adaptive neuro fuzzy inference system
Engineering Artificial neural network business.industry 020209 energy 020208 electrical & electronic engineering Data classification Exponential smoothing Energy Engineering and Power Technology 02 engineering and technology computer.software_genre Fuzzy logic Wavelet packet decomposition Electric power system 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering business Cluster analysis computer |
| Zdroj: | International Journal of Electrical Power & Energy Systems. 77:136-144 |
| ISSN: | 0142-0615 |
| DOI: | 10.1016/j.ijepes.2015.11.046 |
| Popis: | This paper describes daily peak load forecasting using an adaptive hybrid two-stage methodology. Because the time series of electricity consumption is mainly influenced by seasonal effects, the double seasonal Holt–Winters exponential smoothing method is firstly used for next-day peak electricity demand forecasting. In the second stage, the secondary forecasting model is applied taking into account the benefits of Fuzzy c-means clustering; K-nearest neighbors algorithm; Wavelet packet decomposition; and Adaptive Neuro-Fuzzy Inference System, for further improvement in forecasting accuracy. The whole architecture of the proposed model will be presented and the results will be compared with neural networks and stand-alone adaptive neuro-fuzzy inference system based approaches by using a gathered data from the Algerian power system. The results show that: (1) the proposed methodology is the best among all the considered schemes, (2) the FKW-ANFIS has satisfactory performance in both normal and special daily conditions. |
| Databáze: | OpenAIRE |
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