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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Modular electrical demand forecasting framework  a novel hybrid model approach
 Hochschule für Technik, Wirtschaft und Kultur HTWK, Leipzig; Institute of Electrical and Electronics Engineers IEEE: 13th International MultiConference on Systems, Signals & Devices 2016 : March 2124, 2016 in Leipzig, Germany Piscataway, NJ: IEEE, 2016 ISBN: 9781509012916 ISBN: 9781509012909 ISBN: 9781509012923 (Print) pp.454458 
 International MultiConference on Systems, Signals & Devices (SSD) <13, 2016, Leipzig> 

 English 
 Conference Paper 
 Fraunhofer IMW () 
Abstract
In the face of a changing European power market, accurate electric load forecasts are of significant importance for power traders, power utility and grid operators to reduce costs for ancillary services. The following case study, based on publicly available load data, focuses on a novel approach to combine different forecasting methodologies and techniques from the area of computational intelligence. The proposed hybrid model blends input forecasts from artificial neuronal networks, multi variable linear regression and support vector regression machine models with fuzzy sets to intraday and day ahead forecasts. The forecasts are evaluated with commonly used metrics (mean average percentage error  MAPE & normalized rooted mean square error  NRMSE) to allow a comparison to other case studies. The results from the input forecasting models range from a yearly MAPE of 3.1% for the artificial neuronal network to 2.51% for the support vector machine. The blended forecast from the proposed hybrid model results in a MAPE of 1.2% for one hour and a MAPE of 2.03% for 24 hours ahead forecasts.