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

Hernández, Luis; Baladrón Zorita, Carlos; Aguiar Pérez, Javier Manuel; Calavia Domínguez, Lorena; Carro Martínez, Belén; Sanchez-Esguevillas, Antonio; Garcia Fernandez, Pablo; Lloret, Jaime (MDPI, 2013-06)

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 ...
Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Hernández, Luis; Baladrón Zorita, Carlos; Aguiar Pérez, Javier Manuel; Calavia Domínguez, Lorena; Carro Martínez, Belén; Sanchez-Esguevillas, Antonio; Sanjuan, Javier; Gonzalez, Alvaro; Lloret, Jaime (MDPI, 2013-09)

Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where ...
Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Hernández, Luis; Baladrón Zorita, Carlos; Aguiar Pérez, Javier Manuel; Carro Martínez, Belén; Sanchez-Esguevillas, Antonio; Lloret, Jaime (MDPI, 2013-03)

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. ...

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