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Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment
CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, 42290 Lubia, Soria, Spain
Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain
Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 50018 Zaragoza, Spain
Universidad de Zaragoza, Ingeniería Informática, 50018 Zaragoza, Spain
Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain
* Author to whom correspondence should be addressed.
Received: 18 July 2013; in revised form: 18 August 2013 / Accepted: 23 August 2013 / Published: 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
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Cite This Article
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.
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.
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.