Wind power and electricity consumption forecasting on a smart house location
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Fecha
2013-03
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European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)
Resumen
This paper presents the use of an artificial neural network for classification on a residence house that uses wind and electricity consumption predictions to identify patterns at the desired location, in order to obtain a stochastic distribution of the daily wind and electricity profile. This is a step on the further creation of a short-term operation model that allows determining the technical and economic impact of stationary/mobile batteries of electric vehicles in presence of microrenewables along with the electricity consumption. This short-term operation model will be in the day-ahead perfect market operation (unit commitment) where specific changes are made to consider stationary and mobile operation.
Palabras clave
Artificial neural networks
Electricity
Forecasting
Smart homes
Wind energy
Electricity
Forecasting
Smart homes
Wind energy
Descripción
Materias
Cita
Eliasstam, Genikomsakis, & Ioakimidis. (2013). Wind power and electricity consumption forecasting on a smart house location. Renewable Energy and Power Quality Journal, 1(11), 655-659. https://doi.org/10.24084/REPQJ11.404