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Energies 2014, 7(3), 1576-1598; doi:10.3390/en7031576

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

1,* , 2
1 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT—Research Center for Energy, Environment and Technology), Autovía de Navarra A15, Salida 56, 42290 Lubia, Soria, Spain 2 Departamento Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain 3 Departamento Ingeniería Mecánica, Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, Calle María de Luna 5, 50018 Zaragoza, Spain 4 Departamento de Tecnologia Electronica, Universidad Rey Juan Carlos, Escuela Superior de Ciencias Experimentales y Tecnología, Calle Tulipán s/n, 28933 Móstoles, Madrid, Spain 5 Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain
* Author to whom correspondence should be addressed.
Received: 7 February 2014 / Revised: 7 March 2014 / Accepted: 10 March 2014 / Published: 17 March 2014
(This article belongs to the Special Issue Smart Grids: The Electrical Power Network and Communication System)
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The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
Keywords: microgrid; short-term load forecasting; multi-layer perceptron; artificial neural network microgrid; short-term load forecasting; multi-layer perceptron; artificial neural network
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.

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Hernández, L.; Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Pérez, F.; Fernández, Á.; Lloret, J. Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems. Energies 2014, 7, 1576-1598.

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