Some Empirical Evaluations of a Temperature Forecasting Module based on Artificial Neural Networks for a Domotic Home Environment

F. Zamora-Martinez, P. Romeu, J. Pardo, D. Tormo

Abstract

This work presents the empirical evaluation of an indoor temperature prediction module which is integrated in an ambient intelligence control software. This software is running on the SMLhouse, a domotic house built by our university. A study of impact on prediction error of future window size has been performed. We use Artificial Neural Networks models for a multi-step-ahead direct forecasting, using an output size of 60, 120, and 180. Interesting results have been obtained, in the worst case a Mean Absolute Error of 0.223ºC over a validation set, and 0.566ºC over a hard unseen test set. This results inspire the development of an automatic control built over this predictions, that could manage the climate system in order to enhance the comfort and energy efficiency of our house.

References

  1. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
  2. Carney, J., Cunningham, P., Bhagwan, U., and England, L. (1999). Confidence and Prediction Intervals for Neural Network Ensembles. In IEEE IJCNN, pages 1215- 1218.
  3. Cheng, H., Tan, P.-n., Gao, J., and Scripps, J. (2006). Multistep-ahead time series prediction. LNCS, 3918:765-774.
  4. Graves, A. et al. (2009). A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE TPAMI, 31(5):855-868.
  5. Instituto para la diversificación y ahorro de la energía (IDAE) (2011). Practical Guide to Energy. Efficient and Responsible Consumption. Madrid.
  6. Kreuzer, K. and Eichstädt-Engelen, T. (2011). The OSGI-based Open Home Automation Bus. http://www.openhab.org.
  7. Thomas, B. and Soleimani-Mohseni, M. (2006). Artificial neural network models for indoor temperature prediction: investigations in two buildings. Neural Comput. Appl., 16(1):81-89.
  8. Yu, L., Wang, S., and Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5):2623- 2635.
  9. Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1):35-62.
Download


Paper Citation


in Harvard Style

Zamora-Martinez F., Romeu P., Pardo J. and Tormo D. (2012). Some Empirical Evaluations of a Temperature Forecasting Module based on Artificial Neural Networks for a Domotic Home Environment . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 206-211. DOI: 10.5220/0004133502060211


in Bibtex Style

@conference{kdir12,
author={F. Zamora-Martinez and P. Romeu and J. Pardo and D. Tormo},
title={Some Empirical Evaluations of a Temperature Forecasting Module based on Artificial Neural Networks for a Domotic Home Environment},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={206-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004133502060211},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Some Empirical Evaluations of a Temperature Forecasting Module based on Artificial Neural Networks for a Domotic Home Environment
SN - 978-989-8565-29-7
AU - Zamora-Martinez F.
AU - Romeu P.
AU - Pardo J.
AU - Tormo D.
PY - 2012
SP - 206
EP - 211
DO - 10.5220/0004133502060211