ITM Web Conf.
Volume 15, 2017II International Conference of Computational Methods in Engineering Science (CMES’17)
|Number of page(s)||5|
|Section||Computational And Artificial Intelligence|
|Published online||15 December 2017|
Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression
Czestochowa University of Technology, Department of Electrical Engineering, Al. Armii Krajowej 17, 42-200 Czestochowa, Poland
* Corresponding author: firstname.lastname@example.org
Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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