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Energies 2013, 6(9), 4489-4507; doi:10.3390/en6094489
Article

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

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Received: 18 July 2013; in revised form: 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)
Download PDF [1159 KB, uploaded 29 August 2013]
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 artificial neural network; short-term load forecasting; microgrid; multilayer perceptron; peak load forecasting; valley load forecasting; next day’s total load
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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MDPI and ACS Style

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.

AMA Style

Hernández L, Baladrón C, Aguiar JM, 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(9):4489-4507.

Chicago/Turabian Style

Hernández, Luis; Baladrón, Carlos; Aguiar, Javier M.; Calavia, Lorena; Carro, Belén; Sánchez-Esguevillas, Antonio; Sanjuán, Javier; González, Álvaro; Lloret, Jaime. 2013. "Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment." Energies 6, no. 9: 4489-4507.


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