Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Martínez-Arellano, G. ORCID: 0000-0003-3105-4151, Nolle, L., Cant, R. ORCID: 0000-0001-9610-7205, Lotfi, A. ORCID: 0000-0002-5139-6565 and Windmill, C., 2014. Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach. KI - Künstliche Intelligenz, 28 (4), pp. 239-253. ISSN 0933-1875


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Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events.

Item Type: Journal article
Publication Title: KI - Künstliche Intelligenz
Creators: Martínez-Arellano, G., Nolle, L., Cant, R., Lotfi, A. and Windmill, C.
Publisher: Springer
Date: 2014
Volume: 28
Number: 4
ISSN: 0933-1875
Divisions: Schools > School of Science and Technology
Record created by: EPrints Services
Date Added: 09 Oct 2015 09:52
Last Modified: 09 Jun 2017 13:13

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