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Erschienen in:
2019 | OriginalPaper | Buchkapitel
An Indoor Illuminance Prediction Model Based on Neural Networks for Visual Comfort and Energy Efficiency Optimization Purposes
verfasst von : M. Martell, M. Castilla, F. Rodríguez, M. Berenguel
Erschienen in: From Bioinspired Systems and Biomedical Applications to Machine Learning
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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 \(2\%\).