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Energies 2013, 6(6), 2927-2948; doi:10.3390/en6062927

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

1
CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain
2
Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
3
Faculty of Sciences, University of Oviedo, c/Calvo Sotelo s/n, Oviedo 33007, Spain
4
Department of Communications, Polytechnic University of Valencia, Camino Vera s/n 46022, Valencia, Spain
*
Authors to whom correspondence should be addressed.
Received: 29 March 2013 / Revised: 6 June 2013 / Accepted: 6 June 2013 / Published: 17 June 2013
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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 artificial neural network; aggregated load; smart grid; microgrid; multilayer perceptron
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hernández, L.; Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; García, P.; Lloret, J. Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks. Energies 2013, 6, 2927-2948.

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