Load forecast model switching scheme for improved robustness to changes in building energy consumption patterns

Energies

Volumn 6, Issue 3, 2013, Pages 1329-1343

  Yoo, Jaeyeong ^a   Hur, Kyeon ^a  

^a Yonsei University   (South Korea)

Author keywords

Accumulated error; Autoregressive moving average with exogenous variable (ARMAX); Data pattern classification; Kalman filtering; Load forecasting; Model switching scheme (MSS)

Indexed keywords

ELECTRIC POWER PLANT LOADS; ENERGY EFFICIENCY; ENERGY UTILIZATION; ERRORS; FORECASTING; GAUSSIAN NOISE (ELECTRONIC); KALMAN FILTERS; OFFICE BUILDINGS; SWITCHING;

ACCUMULATED ERRORS; AUTOREGRESSIVE MOVING AVERAGE WITH EXOGENOUS VARIABLES; KALMAN-FILTERING; LOAD FORECASTING; MODEL SWITCHING;

CLASSIFICATION (OF INFORMATION);

EID: 84877686108     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en6031329     Document Type: Article

References (21)

1. Clarkea, J.A.; Cockroftb, J.; Connera, S.; Handa, J.W.; Kellya, N.J.; Mooreb, R.; O'Briena, T.; Strachan, P. Simulation-assisted control in building energy management systems. Energy Build. 2002, 34, 933-940.
2. Kolokotsaa, D.; Pouliezosb, A.; Stavrakakisc, G.; Lazosc, C. Predictive control techniques for energy and indoor environmental quality management in buildings. Energy Build. 2009, 44, 1850-1863.
3. Tyler. J.; Auslander, D.M. Improved Methods to Load Prediction in Commercial Buildings. In Proceedings of the 2012 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 12-17 August 2012; pp. 173-184.
4. Vandaele, W. Applied Time Series and Box-Jenkins Model; Academic Press: New York, NY, USA, 1983.
5. Gross, G.; Galiana, F.D. Short term load forecasting. Proc. IEEE 1987, 75, 1588-1573.
6. Kim, K.H.; Park, J.K.; Hwang, K.J.; Kim, S.H. Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Trans. Power Syst. 1995, 10, 1534-1539.
7. Srinivasan, D.; Swee, T.S.; Cheng, C.S.; Chan, E.K. Parallel neural network-fuzzy expert system strategy for short-term load forecasting: System implementation and performance evaluation. IEEE Trans. Power Syst. 1999, 6, 1100-1106.
8. Park, D.C.; El-Sharkawi, M.A.; Marks II, R.J.; Atlas, L.E.; Damborg, M.J. Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 1991, 6, 442-449.
9. Mori, H.; Kosemura, K. Optimal Regression Tree Based Rule Discovery for Short-Term Load Forecasting. In Proceedings of the IEEE Power Engineering Society Winter Meeting, Columbus, OH, USA, 28 January-1 February 2001; pp. 421-426.
10. Rahman, S.; Hazim, O. A generalized knowledge-based short-term load-forecasting technique. IEEE Trans. Power Syst. 1993, 7, 508-514.
11. Lee, K.Y.; Cha, Y.T.; Park, J.H. Short-term load forecasting using an artificial neural network. IEEE Trans. Power Syst. 2005, 7, 2499-2504.
12. Hsu, H.H.; Ho, K.L. Fuzzy expert systems: An application to shortterm load forecasting. Proc. Inst. Elect. Eng. Gen. Transm. Distrib. 1992, 139, 471-477.
13. Yang, H.T.; Huang, C.M.; Huang, C.L. Identification of ARMAX model for short term load forecasting: An evolutionary programming approach. IEEE Trans. Power Syst. 1996, 11, 403-408.
14. Ruzic, S.; Vuckovic, A.; Nikolic, N. Weather sensitive method for short term load forecasting in electric power utility of Serbia. IEEE Trans. Power Syst. 2003, 18, 1581-1586.
15. Herter, K. Residential implementation of critical-peak pricing of electricity. Energy Policy 2007, 35, 2121-2130.
16. Hagan, M.T.; Behr, S.M. The time series approach to short-term load forecasting, IEEE Trans. Power App. Syst. 1987, PAS-2, 785-791.
17. Wang, B.; Tai, N.L.; Zhai, H.Q.; Ye, J.; Zhu, J.D.; Qi, L.B. A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting. Electr. Power Syst. Res. 2008, 78, 1679-1685.
18. Box, G.E.P.; Jenkins, G.M. Time Series AnalysisForecasting and Control; Holden-Day: San Francisco, CA, USA, 1976.
19. Brown, R.G.; Hesterburg, T. Introduction to Random Signal Analysis and Kalman Filtering;Wiley: New York, NY, USA, 1983.
20. Ristic, B.; Arulampalam, S.; Gordon, N. Beyond the Kalman Filter; Artech House: Norwood, MA, USA, 2004.
21. Korea Meteorological Administration. Available online: http://web.kma.go.kr/eng (accessed on 26 February 2013).