Abstract
Aiming at the problems for predicting the building indoor temperature so as to set up a reasonable indoor environment, four building indoor temperature prediction models were established in this paper. The theory of Support Vector Machine (SVM) and the LibSVM toolbox were used to predict the indoor temperature. The experimental results shown that the prediction effect of the model which the input are the outdoor temperature, the solar radiation, the wind speed and the time series, the output is the indoor temperature is the best. It’s really effective to use the support vector regression (SVR) model to predict the building indoor temperature. This predicting method based on SVM can be promoted and applied in the field of prediction.
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© 2014 Springer International Publishing Switzerland
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Wang, W., Cai, Q., Wang, S. (2014). The Research of Building Indoor Temperature Prediction Based on Support Vector Machine. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_10
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DOI: https://doi.org/10.1007/978-3-319-12436-0_10
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