Electric load forecasting for large office building based on radial basis function neural network

IEEE Power and Energy Society General Meeting

Volumn 2014-October, Issue October, 2014, Pages

  Mai, Weijie ^a   Chung, C Y ^a   Wu, Ting ^a   Huang, Huazhang ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

building energy efficiency; commercial office buildings; demand side management; Load forecasting

Indexed keywords

DEMAND SIDE MANAGEMENT; ELECTRIC POWER PLANT LOADS; ELECTRIC UTILITIES; ENERGY EFFICIENCY; FORECASTING; FUNCTIONS; METEOROLOGY; OFFICE BUILDINGS; RADIAL BASIS FUNCTION NETWORKS; SMART POWER GRIDS;

BUILDING ENERGY EFFICIENCY; BUILDING ENERGY SYSTEMS; BUILDING LOAD; INTELLIGENT OPTIMAL CONTROL; LOAD FORECASTING; PARAMETERIZING; RADIAL BASIS FUNCTION NEURAL NETWORKS; TRIAL AND ERROR;

ELECTRIC LOAD FORECASTING;

EID: 84930999562     PISSN: 19449925     EISSN: 19449933     Source Type: Conference Proceeding    
DOI: 10.1109/PESGM.2014.6939378     Document Type: Conference Paper

References (33)

1. S. Lang, "Current situation and progress of energy efficiency design standards in buildings in China," Refrig Air Conditioning & Electr Power Machine, vol. 23, pp. 1-6, 2002 [in Chinese].
2. R. Yao, B. Li and K. Steemers, "Energy policy and standard for built environment in China," Renewable Energy, vol. 30, pp. 1973-1988, 2005.
3. Y. Jiang, "Current building energy consumption in China and effective energy efficiency measures," HV&AC, vol. 35, pp. 30-40, 2005 [in Chinese].
4. Y. Jiang, "Current trend of building energy use and major conservation issues in China," Green Build, vol. 7, pp. 10-15, 2006 [in Chinese].
5. "Buildings energy data book. " [Online]. Available: http://buildingsdatabook. eren. doe. gov/default. Aspx
6. I. Fernandez, C. Borges, and Y. Penya, "Efficient building load forecasting," in Proc. 2011 IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-8.
7. G. M. Masters, Renewable and Efficient Electric Power Systems, Hoboken New Jersey: Wiley, 2004.
8. L. Baizhan, Y. Runming and DJ. Croome, "Air-conditioning in China," Building Research And Information, vol. 23, pp. 309-316, 1995.
9. Y. Iwasaki, S. Kobayashi, A. Nagaiwa, "Development of district heating and cooling plant operation support system," ASHRAE Trans, vol. 104, pp. 5-12, 1998.
10. H. Alfares and M. Nazeeruddin, "Electric load forecasting: literature survey and classification of methods," International Journal of Systems Science, vol. 33, no. 1, pp. 23-34, 2002.
11. V. Hinojosa and A. Hoese, "Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms," IEEE Trans. Power Systems, vol. 25, no. 1, pp. 565-574, 2010.
12. S. Tzafestas and E. Tzafestas, "Computational intelligence techniques for short-term electric load forecasting," Journal of Intelligent and Robotic Systems, vol. 31, pp. 7-68, 2001.
13. E. Feinberg and D. Genethliou, "Load forecasting," Applied Mathematics for Power Systems, chapter 12, pp. 269-285, 2005.
14. D. Infield and D. Hill, "Optimal smoothing for trend removal in short term electricity demand forecasting," IEEE Trans. Power Systems, vol. 13, no. 3, pp. 1115-1120, 1998.
15. G. Mbamalu and M. El-Hawary, "Load forecasting via sub optimal seasonal auto regressive models and iteratively reweighted least squares estimation," IEEE Trans. Power Systems, vol. 8, no. 1, pp. 343-348, 1993.
16. M. Hagan and S. Behr, "The time series approach to short term load forecasting," IEEE Trans. Power Systems, vol. 2, no. 3, pp. 785-791, 1987.
17. H. Yang and C. Huang, "A new short-term load forecasting approach using self-organizing fuzzy ARMAX models," IEEE Trans. Power Systems, vol. 13, no. 1, pp. 217-225, 1998.
18. A. Jain and B. Satish, "Clustering based short term load forecasting using artificial neural network," in Proc. 2009 IEEE PES Power Systems Conference Exposition (PSCE), pp. 1-7.
19. A. Jain and B. Satish, "Clustering based short term load forecasting using support vector machines," in Proc. 2009 IEEE Bucharest PowerTech, pp. 1-8.
20. C. Bo-Juen, C. Ming-Wei, and L. Chih-Jen, "Load forecasting using support vector machines: a study on EUNITE competition 2001," IEEE Trans. Power Systems, vol. 19, no. 4, pp. 1821-1830, 2004.
21. K. Ho, Y. Hsu, C. Chen, T. Lee, C. Liang, T. Lai, and K. Chen, "Shortterm load forecasting of Taiwan power system using a knowledgebased expert system," IEEE Trans. Power Systems, vol. 5, no. 4, pp. 1214-1221, 1990.
22. H. Mori and H. Kobayashi, "Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting," IEEE Trans. Evolutionary Computing, vol. 10, no. 3, pp. 330-340, 2006.
23. S. W. Wang, X. H. Xu, "Simplified building model for transient thermal performance estimation using GA-based parameter identification," International Journal of Thermal Sciences, vol. 45, pp. 419-432, April 2006.
24. S. E. Papadakis, J. B. Theocharis, S. J. Kiartzis, and A. G. Bakirtzis, "A novel approach to short-term load forecasting using fuzzy neural networks," IEEE Trans. Power Systems, vol. 13, no. 2, pp. 480-492, 1998.
25. J. Park, Y. Park, and K. Lee, "Composite modeling for adaptive shortterm load forecasting," IEEE Trans. Power Systems, vol. 6, no. 2, pp. 450-457, 1991.
26. P. Gonzalez and J. Zamarreno, "Prediction of hourly energy consumption in buildings based on a feedback artificial neural network," Energy and Buildings, vol. 37, no. 6, pp. 595-601, 2005.
27. B. Ekici and U. Aksoy, "Prediction of building energy consumption by using artificial neural networks," Advances in Engineering Software, vol. 40, no. 5, pp. 356-362, 2009.
28. P. D. Wasserman, Advanced methods in neural computing, Van Nostrand Reinhold, New York: Wiley, 1993.
29. S. Haykin, Neural networks: a comprehensive foundation, 2nd ed, Prentice Hall, New Jersey, 1999.
30. M. Ohlsson, C. Peterson, H. Pi, T. Rognvaldsson, B. Soderberg, "Predicting System Loads with Artificial Neural Networks-Methods and Results from the Great Energy Predictor Shootout," Tech. Rep. LU T 93-24, Sep. 1993.
31. R. Dodier, G. Henze, "Statistical Analysis of Neural Network as applied to Building Energy Prediction. Energy Systems Laboratory," Tech. Rep. ESL-PA-96/07.
32. "Meteorological Bureau of Shenzhen" [Online]. Available: http://www. szmb. gov. cn/
33. S. Chen, X. Hong and C. J. Harris, "Orthogonal forward selection for constructing the radial basis function network with tunable nodes," in Proc. 2005 international conference on Advances in Intelligent Computing (ICIC'05), vol. 1, pp. 777-786.