Short-term load forecasting in a non-residential building contrasting models and attributes

Energy and Buildings

Volumn 92, Issue , 2015, Pages 322-330

  Massana, Joaquim ^a   Pous, Carles ^a   Burgas, Llorenç ^a   Melendez, Joaquim ^a   Colomer, Joan ^a  

^a UNIVERSITY OF GIRONA   (Spain)

Author keywords

Load forecasting; Measured data; Mediterranean climate; Multilayer perceptron; Neural networks; Regression; Support vector machines

Indexed keywords

BUILDINGS; ECONOMICS; FORECASTING; HOUSING; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

LOAD FORECASTING; MEASURED DATA; MEDITERRANEAN CLIMATES; MULTI LAYER PERCEPTRON; REGRESSION;

ELECTRIC LOAD FORECASTING;

EID: 84923378659     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2015.02.007     Document Type: Article

References (28)

1. C.E. Borges, Y.K. Penya, and I. Fernández Optimal combined short-term building load forecasting Innovative Smart Grid Technologies Asia (ISGT), 2011 IEEE PES 2011 IEEE, DeustoTech (Energy Unit) University of Deusto Bilbao, Spain 1 7
2. X.G. Casals Analysis of building energy regulation and certification in Europe: their role, limitations and differences Energy Build. 38 5 2006 381 392
3. N.R. Draper, and H. Smith Applied Regression Analysis 2nd ed. 1981 John Wiley and Sons
4. G. Escrivá-Escrivá, C. Álvarez Bel, C. Roldán-Blay, and M. Alcázar-Ortega New artificial neural network prediction method for electrical consumption forecasting based on building end-uses Energy Build. 43 11 2011 3112 3119
5. I. Fernandez, C.E. Borges, and Y.K. Penya Efficient building load forecasting ETFA 2011 2011 1 8
6. D.E. Goldberg, and J.H. Holland Genetic algorithms and machine learning Mach. Learn. 3 2 1988 95 99
7. P.A. González, and J.M. Zamarre no Prediction of hourly energy consumption in buildings based on a feedback artificial neural network Energy Build. 37 6 2005 595 601
8. M. Hall, E. Frank, and G. Holmes The WEKA data mining software: an update ACM SIGKDD Explor. Newslett. 11 1 2009 10 18
9. M.A. Hall Correlation-Based Feature Selection for Machine Learning 1999 The University of Waikato (Dissertation)
10. V.A. Kamaev, M.V. Shcherbakov, N.L. Panchenko, D.P. Shcherbakova, and A. Brebels Using connectionist systems for electric energy consumption forecasting in shopping centers Autom. Rem. Control 73 6 2012 1075 1084
11. S. Karatasou, M. Santamouris, and V. Geros Modeling and predicting building's energy use with artificial neural networks: methods and results Energy Build. 38 8 2006 949 958
12. P. Kinney Zigbee technology: wireless control that simply works Communications Design Conference 2003
13. S.S.K. Kwok, R.K.K. Yuen, and E.W.M. Lee An intelligent approach to assessing the effect of building occupancy on building cooling load prediction Build. Environ. 46 8 2011 1681 1690
14. J.C. Lam, K.K.W. Wan, S.L. Wong, and T.N.T. Lam Principal component analysis and long-term building energy simulation correlation Energy Convers. Manage. 51 1 2010 135 139
15. P. Langley Selection of Relevant Features in Machine Learning 1994 Defense Technical Information Center
16. K. Li, and H. Su Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system Energy Build. 42 11 2010 2070 2076
17. K. Li, H. Su, and J. Chu Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study Energy Build. 43 10 2011 2893 2899
18. A.H. Neto, and F.A. Sanzovo Fiorelli Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption Energy Build. 40 12 2008 2169 2176
19. Y.K. Penya, C.E. Borges, and I. Fernandez Short-term load forecasting in air-conditioned non-residential buildings IEEE Africon'11 2011 1 6
20. J.-C. Piris The Lisbon Treaty: A Legal and Political Analysis 2010 Cambridge University Press
21. S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, and K.R.K. Murthy Improvements to the SMO algorithm for SVM regression IEEE Trans. Neural Netw. 11 5 2000 1188 1193
22. A.J. Smola, and B. Schölkopf A tutorial on support vector regression Stat. Comput. 14 3 2004 199 222
23. B. Üstün, W.J. Melssen, and L.M.C. Buydens Facilitating the application of support vector regression by using a universal Pearson VII function based kernel Chemometr. Intell. Lab. Syst. 81 1 2006 29 40
24. V. Vapnik The Nature of Statistical Learning Theory 2000 Springer
25. J. Yang, H. Rivard, and R. Zmeureanu On-line building energy prediction using adaptive artificial neural networks Energy Build. 37 12 2005 1250 1259
26. B. Yegnanarayana Artificial Neural Networks 2009 PHI Learning Pvt. Ltd.
27. H.-x. Zhao, and F. Magoules New parallel support vector regression for predicting building energy consumption. 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM) 2011 14 21
28. H.-x. Zhao, and F. Magoulès Feature selection for predicting building energy consumption based on statistical learning method J. Algorithms Comput. Technol. 6 1 2012 59 78