Abstract
Energy efficiency in buildings is a topic that is being widely studied. In order to achieve energy efficiency it is necessary to perform both, a proper management of the electric demand, and an optimal exploitation of renewable sources, using for that appropriate control strategies. The main objective of this paper is to develop a short term predictive model, based on neural networks, of the electricity demand for the CIESOL research center. The performed experiments, using different techniques for weather forecast, show a quick prediction with acceptable final results for real data, obtaining a maximum root mean squared error of 5 % in validation data, with a short-term prediction horizon of 60 minutes.
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Yedra, R.M., Díaz, F.R., del Mar Castilla Nieto, M., Arahal, M.R. (2014). A Neural Network Model for Energy Consumption Prediction of CIESOL Bioclimatic Building. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_6
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DOI: https://doi.org/10.1007/978-3-319-01854-6_6
Publisher Name: Springer, Cham
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