RT info:eu-repo/semantics/article
T1 Experimental analysis of the input variables’ relevance to forecast next day’s aggregated electric demand using  neural networks
A1 Hernández Callejo, Luis
A1 Baladrón García, Carlos
A1 Aguiar Pérez, Javier Manuel
A1 Calavia, Lorena
A1 Carro Martínez, Belén
A1 Sánchez Esguevillas, Antonio Javier
A1 García, Pablo
A1 Lloret, Jaime
K1 Artificial Neural Networks (ANN)
K1 Aggregated load
K1 Smart grid
K1 Microgrids
K1 Multilayer perceptron
K1 33 Ciencias Tecnológicas
AB 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. 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.
PB MDPI
SN 1996-1073
YR 2013
FD 2013
LK https://uvadoc.uva.es/handle/10324/57642
UL https://uvadoc.uva.es/handle/10324/57642
LA eng
NO Energies, 2013, vol. 6, n. 6, p. 2927-2948
NO Producción Científica
DS UVaDOC
RD 12-sep-2024