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

Luis Hernández, Carlos Baladrón, Javier M. Aguiar, Lorena Calavia, Belén Carro, Antonio Sánchez-Esguevillas, Javier Sanjuán, Álvaro González and Jaime Lloret
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Luis Hernández: CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, 42290 Lubia, Soria, Spain
Carlos Baladrón: Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Javier M. Aguiar: Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Lorena Calavia: Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Belén Carro: Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Antonio Sánchez-Esguevillas: Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Javier Sanjuán: Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 50018 Zaragoza, Spain
Álvaro González: Universidad de Zaragoza, Ingeniería Informática, 50018 Zaragoza, Spain
Jaime Lloret: Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain

Energies, 2013, vol. 6, issue 9, 1-19

Abstract: 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 detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids . This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

Keywords: artificial neural network; short-term load forecasting; microgrid; multilayer perceptron; peak load forecasting; valley load forecasting; next day’s total load (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: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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