Abstract
Energy and comfort management are becoming increasingly relevant topics into buildings operation, for example, looking for trade-off solutions to maintain adequate comfort conditions within an efficient energy use framework by means of appropriate control and optimization techniques. Moreover, these strategies can take advantage from predictions of the involved variables. In this regard, visual comfort conditions are a key aspect to consider. Hence, in this paper an indoor illuminance prediction model based on a divide-and-rule strategy which makes use of Artificial Neural Networks and polynomial interpolation is proposed. This model has been trained, validated and tested using real data gathered in a bioclimatic building. As a result, an acceptable forecast of indoor illuminance level was obtained with a mean absolute error equals to 8.9 lx and a relative error lower than \(2\%\).
This work has been funded by the National R+D+i Plan Project DPI2017-85007-R of the Spanish Ministry of Science, Innovation and Universities and ERDF funds.
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References
Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design and application. J. Microbiol. Methods 43, 3–31 (2000)
Castilla, M., Álvarez, J.D., Ortega, M.G., Arahal, M.R.: Neural network and polynomial approximated thermal comfort models for HVAC systems. Build. Environ. 59, 107–115 (2013)
Castilla, M., Álvarez, J.D., Rodríguez, F., Berenguel, M.: Comfort Control in Buildings. Advances in Industrial Control. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6347-3. ISBN 978-1-4471-6346-6
Castilla, M., Bordons, C.: Optimal management of a microgrid to guarantee users’ thermal comfort. In: International Conference on Smart Energy Systems and Technologies (SEST 2018), Sevilla, Spain (2018)
Chen, S., Billings, S.A.: Neural networks for nonlinear dynamic system modelling and identification. Int. J. Control 56, 319–346 (1992)
EN-12665: Light and lighting: basic terms and criteria for specifying lighting requirements. European Committee for Standardization, Brussels (2018)
Kazanasmaz, Z.T., Gnaydin, M., Binol, S.: Artificial neural networks to predict daylight illuminance in office buildings. Build. Environ. 44(8), 1751–1757 (2009)
Logar, V., Kristl, Z., \(\hat{S}\)krjanc, I.: Using a fuzzy black-box model to estimate the indoor illuminance in buildings. Energy Build. 70, 343–351 (2014)
Martell, M.: Multiobjective optimization of comfort and energy efficiency in sustainable buildings (in Spanish). Diploma thesis, University of Almería (2017)
Mena, R., Rodríguez, F., Castilla, M., Arahal, M.R.: A prediction model based on neural networks for the energy consumption of a bioclimatic buildings. Energy Build. 82, 142–155 (2014). https://doi.org/10.1016/j.enbuild.2014.06.052
Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. LNM, vol. 630, pp. 105–116. Springer, Heidelberg (1978). https://doi.org/10.1007/BFb0067700
W.E.C. United Nations Department of Economic & Social Affairs, World Energy Assessment. Energy and the Challenge of Sustainability, United Nations Development Programme (2000)
https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-economic-governance-monitoring-prevention-correction/european-semester/framework/europe-2020-strategy_en. Accessed 22 Feb 2019
Eurostat. Final energy consumption by sector. https://ec.europa.eu/eurostat. Accessed 22 Feb 2019
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Martell, M., Castilla, M., Rodríguez, F., Berenguel, M. (2019). An Indoor Illuminance Prediction Model Based on Neural Networks for Visual Comfort and Energy Efficiency Optimization Purposes. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_15
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