Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting

Energy

Volumn 49, Issue 1, 2013, Pages 279-288

  An, Ning ^a   Zhao, Weigang ^b   Wang, Jianzhou ^b   Shang, Duo ^c   Zhao, Erdong ^d  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b LANZHOU UNIVERSITY   (China)

^c STONY BROOK UNIVERSITY   (United States)

^d NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Electricity demand forecasting; EMD based signal filtering; Feedforward neural network; Multi output forecasting; Seasonal adjustment

Indexed keywords

ELECTRICITY; FEEDFORWARD NEURAL NETWORKS; SIGNAL FILTERING AND PREDICTION;

AUSTRALIA; ELECTRICITY DEMAND FORECASTING; ELECTRICITY DEMANDS; EMPIRICAL MODE DECOMPOSITION; ERROR AMPLIFICATION; FORECASTING ACCURACY; MULTI-OUTPUT; NEW SOUTH WALES; NOISE SIGNALS; SEASONAL ADJUSTMENTS; SIGNAL FILTERING;

FORECASTING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DEMAND ANALYSIS; ELECTRICITY GENERATION; ELECTRICITY SUPPLY; EMPIRICAL ANALYSIS; SIGNAL;

AUSTRALIA; NEW SOUTH WALES;

EID: 84871717701     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2012.10.035     Document Type: Article

References (31)

1. Zhu S., Wang J., Zhao W., Wang J. A seasonal hybrid procedure for electricity demand forecasting in China. Applied Energy 2011, 88(11):3807-3815.
2. Papalexopoulos A.D., Hesterberg T.C. A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems 1990, 5(4):1535-1547.
3. Zhou P., Ang B.W., Poh K.L. A trigonometric Grey prediction approach to forecasting electricity demand. Energy 2006, 31(14):2839-2847.
4. Akay D., Atak M. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy 2007, 32(9):1670-1675.
5. Azadeh A., Saberi M., Seraj O. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran. Energy 2010, 35(6):2351-2366.
6. Afshar K., Bigdeli N. Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy 2011, 36(5):2620-2627.
7. Srinivasan D. Energy demand prediction using GMDH networks. Neurocomputing 2008, 72(1-3):625-629.
8. Azadeh A., Ghaderi S.F., Sohrabkhani S. A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy Policy 2008, 36(7):2637-2644.
9. Cai Y., Wang J.-Z., Tang Y., Yang Y.-C. An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherical ARTMAP network) neural network. Energy 2011, 36(2):1340-1350.
10. Wang S., Yu L., Tang L., Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 2011, 36(11):6542-6554.
11. Srinivasan D, Lee MA. Survey of hybrid fuzzy neural approaches to electric load forecasting. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. vol. 5. Vancouver, BC, Canada; 1995, p. 4004-8.
12. Yao A.W.L., Chi S.C., Chen C.K. Development of an integrated Grey-fuzzy-based electricity management system for enterprises. Energy 2005, 30(15):2759-2771.
13. Kumara U., Jain V.K. Time series models (Grey-Markov, Grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy 2010, 35(4):1709-1716.
14. Wang J., Zhu S., Zhang W., Lu H. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 2010, 35(4):1671-1678.
15. Amjady N., Keynia F. Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 2009, 34(1):46-57.
16. Nguyen H.T., Nabney I.T. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. Energy 2010, 35(9):3674-3685.
17. Che J., Wang J., Wang G. An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting. Energy 2012, 37(1):657-664.
18. Guo Z., Zhao W., Lu H., Wang J. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy 2012, 37(1):241-249.
19. Dong Y., Wang J., Jiang H., Wu J. Short-term electricity price forecast based on the improved hybrid model. Energy Conversion and Management 2011, 52(8-9):2987-2995.
20. Boudraa A.-O., Cexus J.-C. EMD-based signal filtering. IEEE Transactions on Instrumentation and Measurement 2007, 56(6):2196-2202.
21. Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical. Physical & Engineering Sciences 1998, 454(1971):903-995.
22. An X., Jiang D., Li S., Zhao M. Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine. Energy 2011, 36(9):5508-5520.
23. Niu D., Cao S., Zhao L., Zhang W. Power load forecasting technology and its applications 1998, [in Chinese], China Electric Power Press, Beijing.
24. Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy 2010, 35(2):512-517.
25. Kalogirou S.A., Bojic M. Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 2000, 25(5):479-491.
26. Kiani M.K.D., Ghobadian B., Tavakoli T., Nikbakht A.M., Najafi G. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends. Energy 2010, 35(1):65-69.
27. Mohandes M.A., Rehman S., Halawani T.O. A neural networks approach for wind speed prediction. Renewable Energy 1998, 13(3):345-354.
28. Andrawis R.R., Atiya A.F., El-Shishiny H. Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting 2011, 27(3):672-688.
29. Bontempi G., Taieb S.B. Conditionally dependent strategies for multiple-step-ahead prediction in local learning. International Journal of Forecasting 2011, 27(3):689-699.
30. Cadenas E., Rivera W. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renewable Energy 2009, 34(1):274-278.
31. Plumb A.P., Rowe R.C., York P., Brown M. Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm. European Journal of Pharmaceutical Sciences 2005, 25(4-5):395-405.