Open Access
Issue
EPJ Web of Conferences
Volume 33, 2012
2nd European Energy Conference
Article Number 05009
Number of page(s) 8
Section End Use of Energy
DOI https://doi.org/10.1051/epjconf/20123305009
Published online 02 October 2012
  1. United Nations Environment Programme, Buildings Can Play Key Role In Combating Climate Change (2007). http://www.unep.org/Documents.Multilingual/Default.asp?DocumentID=502&ArticleID=5545&l=en.
  2. Yudelson J., Greening Existing Buildings, Green Source/McGraw-Hill, New York (2010).
  3. European Union, Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings. Official Journal of the European Communities (2003).
  4. Pedersen L., Use of different methodologies for thermal load and energy estimations in buildings including meteorological and sociological input parameters. Renewable and Sustainable Energy Reviews, Elsevier, 11, 998–1007 (2007). [CrossRef]
  5. Rabl A., Rialhe A., Energy signature models for commercial buildings: test with measured data and interpretation. Energy and Buildings, Elsevier, 19, 143–154 (1992). [CrossRef]
  6. Jackson, Peter, Introduction To Expert Systems (3 ed.), Addison Wesley, p. 2, ISBN 978-0-20187686-4 (1998).
  7. Arbib M.A., The Handbook of Brain Theory and Neural Networks, The MIT Press, Cambridge (MA) (1995).
  8. Haykin S., Neural Networks, a comprehensive foundation (2nd edition), Prentice Hall, New Jersey (1999).
  9. Caldera M., Corgnati S. P., Filippi M., Energy demand for space heating trough a statistical approach: application to residential buildings. Energy and Buildings, Elsevier, 40, 1972–1983 (2008). [CrossRef]
  10. Rauhala K., A simple computer model for estimating the energy consumption of residential buildings in different microclimatic conditions in cold regions. Energy and Buildings, Elsevier, 15–16, 561–569 (1991).
  11. Farahbakhsh H., Ugursal V. I., Fung A. S., A residential end-use energy consumption model for Canada. International Journal of Energy Research, 22, 1133–1143 (1998). [CrossRef]
  12. Mihalakakou G., Santamouris M., Tsangrassoulis A., On the energy consumption in residential buildings. Energy and Buildings, Elsevier, 34, 727–736 (2002). [CrossRef]
  13. Tso G. K. F., Yau K. K. W., Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. Energy, Elsevier, 32, 1761–1768 (2007). [CrossRef]
  14. Rosenblatt, Frank, The Perceptron--a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory (1957).
  15. Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC (1961)
  16. Krogh A., Vedelsby J., Neural network ensembles, cross validation and active learning, in Tesauro G., Touretzky D. S., Leen T. K., editors, Advances in Neural Information Processing Systems, 7, 231–238, MIT Press (1995).
  17. Liu Y., Yao X., Ensemble learning via negative correlation. Neural Networks, 12(10), 1399–1404 (1999). [CrossRef]
  18. Breiman L., Combining Predictors, in Sharkey A.J.C., (ed.): Combining Artificial Neural Nets-Ensemble and Modular Multi-net Systems, Springer, Berlin, 31–50 (1999).
  19. Perrone M. P. e Cooper L. N.. When networks disagree: ensemble methods for hybrid neural networks. Neural Networks for Speech and Image Processing by R.J. Mammone, ed. Chapman-Hall (1993).
  20. Bishop C. M.. Neural Networks for Pattern Recognition. Oxford University Press, 364 – 369 (1995).
  21. Kohavi R. e Bauer E. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36, 105 – 142 (1999). [CrossRef]
  22. Drucker H. Improving regressors using boosting techniques. In Douglas H. Fisher, ed., ICML, Morgan Kaufmann 107 – 115 (1997).
  23. Avnimelech R. and Intrator N. Boosting regression estimators. Neural Computation, 11 491 – 513 (1999).

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.