The daily and hourly energy consumption and load forecasting using artificial neural network method: A case study using a set of 93 households in Portugal

Energy Procedia

Volumn 62, Issue , 2014, Pages 220-229

  Rodrigues, Filipe ^a,b   Cardeira, Carlos ^c   Calado, J M F ^a,c  

^a INSTITUTO POLITÉCNICO DE LISBOA   (Portugal)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Artificial neural networks; Boolean application; Energy forecasting; Hourly and daily energy; Levenberg marquardt

Indexed keywords

EID: 84923412468     PISSN: 18766102     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.egypro.2014.12.383     Document Type: Conference Paper

References (27)

1. H. S. Hippert, C. E. Pedreira, and R. C. Souza, Neural networks for short-term load forecasting: a review and evaluation, IEEE Transactions on Power Systems, 16(1), 44-55, 2001.
2. Carpinteiro, O., Reis, A., Silva, A., A hierarchical neural model in short-term load forecasting, Applied Soft Computing 4, 405-412, 2004.
3. G. Gross and F. D. Galiana, Short-term load forecasting, Proceedings of the IEEE, 75(12), 1558-1573; 1987.
4. Taylor, J.W., Short-Term Load Forecasting with Exponentially Weighted Methods, IEEE Transactions on Power Systems; Volume 27; N 1; Feb. 2012; Page(s) 458-646; 2012.
5. E. A. Feinberg and D. Genethliou, Load forecasting, chapter 12 in Applied Mathematics for Restructured Electric Power Systems: Optimization Control, and Computational Intelligence, Springer-Verlag, New York, 2005.
6. A. S. AlFuhaid, M. A. El-Sayed, and M. S. Mahmoud, "Cascaded artificial neural networks for short-term load forecasting," IEEE Transactions on Power Systems, 12(4), 1524-1529; 1997.
7. Aydinalp M, Ugursal VI, Fung A. Modeling of the appliance, lighting, and space cooling energy consumptions in the residential sector using neural networks. Applied Energy 2002; 72(2):87-110.
8. M. Beccali, M. Cellura, V. Lo Brano, A. Marvuglia "Forecasting daily urban electric load profiles using artificial neural networks", Energy Conversion and Management, vol. 45, pp 2879-2900, 2004.
9. M. Beccali, M. Cellura, V. Lo Brano, A. Marvuglia "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area", Renewable and Sustainable Energy Reviews, vol. 12, pp 2040-2065, 2008.
10. Tso GKF, Yau KKW. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 2005; vol 28 pp 1761-1768.
11. Aydinalp M, Ugursal VI. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Applied Energy; vol. 85 pp 271-296, 2008.
12. Khotanzad, .A, Afkhami-Rohani, R., Abaye, A., Davis, M., Maratukulam D., ANNSTLF-A Neural-Network-Based Electric Load Forecasting System, IEEE Transactions on Neural Networks, Vol.8, N 4, 835-846; 1997.
13. Kalogirou SA. Applications of artificial neural networks in energy systems: A review. Ennergy Conversion and Management 1999; vol. 40 pp 1073-1087.
14. Kalogirou SA. Applications of artificial neural-networks for energy systems. Applied Energy 2000 vol. 67(1-2) pp17-35.
15. Kalogirou SA, Artificial neural networks energy systems applications. Renewable and Sustainable Energy Reviews 2001; vol. 5 pp 373-401.
16. SIETSMA, J., and DOW, R.J.F., Neural Net Pruning: Why and How. In Proceedings of IEEE Int. Conf. Neural Networks, San Diego, Vol.1, pp. 325-333, 1988
17. L.M. Saini, M.K. Soni, Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods, IEE Proceedings General Transmission Distribution. 149 (5) 578-584, 2002.
18. Sarle WS. Stopped training and other remedies for overfitting. Proceedings of the 27th symposium on the interface of computing science and statistic.
19. A. G. Bakirtzis, V. Petridis, S. J. Kiartzis, and M. C. Alexiadis, A neural network short term load forecasting model for the Greek power system, IEEE Transactions on Power Systems, 11(2), 858-863, 1996.
20. William Holderbaum, Régis Canart and Pierre Borne Artificial Neural Networks Application to Boolean Input Systems Control, Laboratoire d'Automatique et d'Informatique Industrielle de Lille, June 1999: http://sic.ici.ro/sic1999-2/art03.html.
21. William Soares Lacerda Conference at Universidade Federal de Lavras (UFLA), Departamento de Ciência da Computação, November 2006.
22. Ricardo S. Zebulum, Marley Vellasco, Karla Guedes, Marco Aurélio Pacheco "Short-term load forecasting using neural nets" International Workshop on Artificial Neural Networks Malaga-Torremolinos, Spain, June 7-9, 1995 Proceedings Natural to Artificial Neural Computation Springer Berlin Heidelberg, ISBN 978-3-540-49288-7, Vol 930 pp 1001-1008.
23. Afkhami, R.; Yazdi, F.M. "Application of neural networks for short-term load forecasting" Power India Conference, 2006 IEEE; 10-12 April 2006.
24. Yu-Jun He; You-Chan Zhu; Jian-Cheng Gu; Cheng-Qun Yin; "Similar day selecting based neural network model and its application in short-term load forecasting". Proceedings of International Conference on Machine Learning and Cybernetics, 2005; Volume 8; 18-21 Aug. 2005; Page(s) 4760-4763; Vol. 8.
25. Ramezani, M.; Falaghi, H.; Haghifam, M.-R.; Shahryari, G.A., "Short-Term Electric Load Forecasting Using Neural Networks".The International Conference on Computer as a Tool, 2005. EUROCON 2005; Volume 2; 21-24 Nov. 2005; Page(s) 1525-1528.
26. Barghinia, S.; Ansarimehr, P.; Habibi, H.; Vafadar, N., "Short term load forecasting of Iran national power system using artificial neural network" Power Tech Proceedings, 2001 IEEE Porto; Volume 3; 10-13 Sept. 2001.
27. Mikhalakakou G, Santamouris M, Tsangrassoulis A. On the energy consumption in residential buildings. Energy and Buildings 2001; vol. 34(7) pp727-36.