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Open AccessArticle

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

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CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, 42290 Lubia, Soria, Spain
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Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
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Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 50018 Zaragoza, Spain
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Universidad de Zaragoza, Ingeniería Informática, 50018 Zaragoza, Spain
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Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Energies 2013, 6(9), 4489-4507; https://doi.org/10.3390/en6094489
Received: 18 July 2013 / Revised: 18 August 2013 / Accepted: 23 August 2013 / Published: 29 August 2013
(This article belongs to the Special Issue Smart Grids: The Electrical Power Network and Communication System)
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. View Full-Text
Keywords: artificial neural network; short-term load forecasting; microgrid; multilayer perceptron; peak load forecasting; valley load forecasting; next day’s total load artificial neural network; short-term load forecasting; microgrid; multilayer perceptron; peak load forecasting; valley load forecasting; next day’s total load
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Hernández, L.; Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Sanjuán, J.; González, Á.; Lloret, J. Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment. Energies 2013, 6, 4489-4507.

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