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

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

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Received: 29 March 2013; in revised form: 6 June 2013 / Accepted: 6 June 2013 / Published: 17 June 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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
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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.

AMA Style

Hernández L, Baladrón C, Aguiar JM, 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(6):2927-2948.

Chicago/Turabian Style

Hernández, Luis; Baladrón, Carlos; Aguiar, Javier M.; Calavia, Lorena; Carro, Belén; Sánchez-Esguevillas, Antonio; García, Pablo; Lloret, Jaime. 2013. "Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks." Energies 6, no. 6: 2927-2948.

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