Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks

Luis Hernández, Carlos Baladrón, Javier M. Aguiar, Lorena Calavia, Belén Carro, Antonio Sánchez-Esguevillas, Pablo García and Jaime Lloret
Additional contact information
Luis Hernández: CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain
Carlos Baladrón: Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Javier M. Aguiar: Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Lorena Calavia: Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Belén Carro: Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Antonio Sánchez-Esguevillas: Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Pablo García: Faculty of Sciences, University of Oviedo, c/Calvo Sotelo s/n, Oviedo 33007, Spain
Jaime Lloret: Department of Communications, Polytechnic University of Valencia, Camino Vera s/n 46022, Valencia, Spain

Energies, 2013, vol. 6, issue 6, 1-22

Abstract: Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present), the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far). This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant for the aggregated. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.

Keywords: artificial neural network; aggregated load; smart grid; microgrid; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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