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

Luis Hernández, Carlos Baladrón, Javier M. Aguiar, Lorena Calavia, Belén Carro, Antonio Sánchez-Esguevillas, Francisco Pérez, Ángel Fernández and Jaime Lloret
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Luis Hernández: 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
Carlos Baladrón: 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
Javier M. Aguiar: 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
Lorena Calavia: 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
Belén Carro: 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
Antonio Sánchez-Esguevillas: 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
Francisco Pérez: Departamento Ingeniería Mecánica, Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, Calle María de Luna 5, 50018 Zaragoza, Spain
Ángel Fernández: Departamento de Tecnologia Electronica, Universidad Rey Juan Carlos, Escuela Superior de Ciencias Experimentales y Tecnología, Calle Tulipán s/n, 28933 Móstoles, Madrid, Spain
Jaime Lloret: Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain

Energies, 2014, vol. 7, issue 3, 1-23

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

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