An integrated simulated-based fuzzy regression algorithm and time series for energy consumption estimation with non-stationary data and case studies

2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09

Volumn , Issue , 2009, Pages 139-143

  Tajvidi, A ^a,c   Azadeh, A ^b,c   Saberi, M ^e   Izadbakhsh, H ^d   Danesh, B ^a   Gitiforouz, A ^e  

^a Azad University of Tafresh   (Iran)

^b UNIVERSITY OF TEHRAN   (Iran)

^c TAFRESH UNIVERSITY   (Iran)

^d IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

^e NONE

Author keywords

[No Author keywords available]

Indexed keywords

ARIMA MODELS; ARTIFICIAL NEURAL NETWORK; AUTOCORRELATION FUNCTIONS; CONVENTIONAL METHODS; DATA PREPROCESSING; ELECTRICITY DEMANDS; ELECTRICITY-CONSUMPTION; ENERGY CONSUMPTION; FUZZY REGRESSION MODELS; FUZZY REGRESSIONS; INPUT VARIABLES; INTEGRATED ALGORITHM; MONTHLY CHANGE; NON-LINEAR MODEL; NON-LINEARITY; NONSTATIONARY DATA; POST PROCESSING; TIME SERIES MODELS; TRIAL-AND-ERROR METHOD; UNIQUE FEATURES;

ALGORITHMS; BACKPROPAGATION; COMPUTATIONAL METHODS; DEVELOPING COUNTRIES; ELECTRIC NETWORK ANALYSIS; ELECTRIC POWER UTILIZATION; ESTIMATION; NEURAL NETWORKS; RANDOM PROCESSES; RANDOM VARIABLES; REGRESSION ANALYSIS; SIMULATORS; TIME SERIES;

ELECTRIC LOAD FORECASTING;

EID: 71649093726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEST.2009.5276675     Document Type: Conference Paper

References (39)

1. Liang, Z., Yang, K., Sun,Y., Yuan, J., Zhang, H., 2006. Zhang, Z., 2006. Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market. JEPO 34 3359-3364
2. Cheng F. Leea,-, Sue J. Linb, Charles Lewisc, Yih F. Chang, 2007. Effects of carbon taxes on different industries by fuzzy goal programming: A case study of the petrochemical-related industries, Taiwan, JEPO, Volume 35, Issue 8, Pages 4051-4058.
3. Sadeghi, M., Mirshojaeian Hosseini, H., 2006. Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs). JEPO, 34 993-1003
4. Canyurta, O.E, Ozturka, H.K, Hepbaslib, A., Utlu, Z., 2005. Estimating the Turkish residential - commercial energy output based on genetic algorithm (GA) approaches. JEPO, 33 1011-1019
5. Jamal O. Jabera, , Rustom Mamlookb, Wa'el Awadc, 2005. Evaluation of energy conservation programs in residential sector using fuzzy logic methodology. JEPO, 33 (2005) 1329-1338
6. Hamzac-ebi, C., 2007.Forecasting of Turkey's net electricity energy consumption on sectoral bases, JEPO, 35 2009-2016
7. Haldenbilen, S., Ceylan, H., 2005. Genetic algorithm approach to estimate transport energy demand in Turkey. JEPO, 33 89-98
8. Murat, Y.S., Ceylan, H., 2006. Use of artificial neural networks for transport energy demand modeling. JEPO, 34 3165-3172.
9. Ross, T. J., 1995.Fuzzy Logic with Engineering Applications. McGraw-Hill, New York,.
10. Tseng, F.M., Yu, H.C., Tzeng, G.H., 2002.Combining neural network model with seasonal time series ARIMA model, Technological Forecasting & Social Change 69: 71-87.
11. Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S., 2004.A neuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology 291: 52-66.
12. Niskaa, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmainena, M., 2004.Evolving the neural network model for forecasting air pollution time series, Engineering Applications of Artificial Intelligence 17:159-167.
13. Karunasinghea, D.S.K., Liong, S.Y., 2006.Chaotic time series prediction with a global model: Artificial neural network, Journal of Hydrology 323: 92-105.
14. Oliveira, A.L.I., Meira, S.R.L., 2006.Detecting novelties in time series through neural networks forecasting with robust confidence intervals, Neurocomputing 70: 79-92.
15. Gareta, R., Romeo, L.M., Gil, A., 2006.Forecasting of electricity prices with neural networks, Energy Conversion and Management 47: 1770-1778.
16. Aznarte, J.L., Sanchez, J.M.B., Lugilde, D.N., Fernandez, C.D.L., Guardia, C.D., Sanchez, F.A., 2007.Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models, 32: 1218-1225.
17. Jain, A., Kumar, A.M., 2007.Hybrid neural network models for hydrologic time series forecasting, Applied Soft Computing 7 :585-592 .
18. Chang, P.T., Konz, S.A., Lee, E.S., 1996.Applying fuzzy linear regression to VDT legibility, Fuzzy Sets and Systems 80: 197-204.
19. Lai, Y.J., Chang, S.I., 1994.A fuzzy approach for multiresponse optimization: An off-line quality engineering problem, Fuzzy Sets and Systems 63: 117-129.
20. Zhang, G., Hu, M.Y., 1998.Neural Network Forecasting of the British Pound/US Dollar Exchange Rate, Omega , The International Journal of Management Science 26:495-506.
21. Al-Saba, T., El-Amin, I., 1999.Artificial neural networks as applied to long-term demand forecasting, Artificial Intelligence in Engineering 13: 189-197.
22. Zhang, G.P., 2001.An investigation of neural networks for linear time-series forecasting, Computers & Operations Research 28:1183-1202.
23. Hwarng, H.B., 2001.Insights into neural-network forecasting of time series corresponding to ARMA (p; q) structures, Omega 29: 273-289.
24. Kim, T.Y., Oh, K.J., Kim, C., Do, J.D., 2004.Artificial neural networks for non-stationary time Series, Neurocomputing 61: 439-447.
25. Zhang, G. P., Qi, M., 2005. Neural network forecasting for seasonal and trend time series, European Journal of Operational Research 160: 501-514.
26. Ghiassi, M., Saidane, H., Zimbra, D. K., 2005.A dynamic artificial neural network model for forecasting time series events, International Journal of Forecasting 21: 341-362.
27. Palmer, A., Montano, J.J., Sese, A., 2006.Designing an artificial neural network for forecasting tourism time series, Tourism Management 27: 781-790.
28. Box, G. E. P., Jenkins, G. M., 1970. Time Series Analysis: Forecasting and Control. San Franciscoed. 1976).
29. Chatfield, C., 2003. The analysis of time series: an introduction.
30. Box, G.E.P., Jenkins, G.M., Reinsel, G.C. Time Series Analysis: Forecasting and Control. Prentice-Hall, Englewood Cliffs, NJ, 1994.2
31. McLeod, A.I., Li, W.K., 1983. Diagnostic checking ARMA time series models using squared-residual autocorrelations. J. Time Ser. Anal. 4, 269-273. Monti, A.C., 1994.
32. Granger, C. W. J. and Newbold, P.1974. Spurious regressions in econometrics. Journal of Econometrics, 2: 111-120.
33. Azadeh, A., Ghaderi, S.F., Anvari, M., Saberi, M., 2007a.Performance assessment of electric power generations using an adaptive neural network algorithm, Energy Policy 35: 3155-3166.
34. Azadeh, A., Ghaderi, S.F., Tarverdian, S., Saberi, M., 2007b.Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption, Applied Mathematics and Computation 186: 1731-1741 .
35. Azadeh, A., Ghaderi, S.F., Sohrabkhani, S., 2007c.Forecasting electrical consumption by integration of neural network, time series and ANOVA, Applied Mathematics and Computation; 186: 1753-1761.
36. Azadeh, A., Tarverdian, S., 2007d. Integration of Genetic Algorithm, Computer Simulation and Design of experiment for Forecasting Electrical Consumption. Energy Policy; 35(10): 5229-5241. .
37. Azadeh, A., Ghaderi, S.F., Anvari, M., Saberi, M., 2006a.Measuring performance electric power generations using artificial neural networks and fuzzy clustering", in G.A Capolino, LG Franquelo(eds) proc. of the 32nd Annual Conference of the IEEE Industrial Electronics Society, IECON, Paris, France.
38. Azadeh, A., Ghaderi, S.F., Anvari, M., Saberi, M., Izadbakhsh, H., 2006b.An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units, Applied Mathematics and Computation 2007; Volume 187, Issue 2 :584-599.
39. Azadeh, A., Ghaderi, S.F., Tarverdian, S., Saberi, M., 2006c.Integration of artificial neural networks and GA to predict electrical energy consumption", in G.A Capolino, L.G Franquelo(eds) proc. of the 32nd Annual Conference of the IEEE Industrial Electronics Society - IECON'06, Conservatoire National des Arts & Metiers - Paris - France .