A new hybrid correction method for short-term load forecasting based on ARIMA, SVR and CSA

References

• Al-Hamadi, H. M., & Soliman, S. A. (2004). Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electric Power Systems Research, 68(1), 47–59.
   Web of Science ®Google Scholar
• Amjady, N. (2007). Short-term bus load forecasting of power systems by a new hybrid method. IEEE Transactions on Power Systems, 22(1), 331–341.
   Web of Science ®Google Scholar
• Box, G. E. P., & Jenkins, G. (1970). Time series analysis, forecasting and control. San Francisco, CA: Holden-Day.
   Google Scholar
• Brown, C., Liebovitch, L. S., & Glendon, R. (2007). Lévy flights in Dobe Juhoansi foraging patterns. Human Ecology, 35, 129–138.
   Web of Science ®Google Scholar
• Bunn, D. W., & Farmer, E. D. (1985). Comparative models for electrical load forecasting. New York, NY: Wiley.
   Google Scholar
• Chapelle, O., & Vapnik, V. (2001). Choosing multiple parameters for support vectors machines. New York, NY: AT&T Research Labs.
   Google Scholar
• Fan, S., Chen, L., & Lee, W. J. (2009). Short-term load forecasting using comprehensive combination based on multimeteorological information. IEEE Transactions on Industry Applications, 45(4), 1460–1466.
   Web of Science ®Google Scholar
• Hansen, J. V., & McDonald, J. B. (2010). Some experimental evidence on the performance of GA-designed neural networks. Journal of Experimental & Theoretical Artificial Intelligence, 13(3), 307–321.
   Web of Science ®Google Scholar
• Hansena, J. V., & Nelson, R. D. (2003). Time-series analysis with neural networks and ARIMA-neural network hybrids. Journal of Experimental & Theoretical Artificial Intelligence, 15(3), 315–330.
   Web of Science ®Google Scholar
• Hong, W. C. (2009). Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion and Management, 50, 105–117.
   Web of Science ®Google Scholar
• Hong, W. C., & Pai, P. F. (2007). Potential assessment of the support vector regression technique in rainfall forecasting. Water Resources Management, 21(2), 495–513.
   Web of Science ®Google Scholar
• Huang, S.-J., & Shih, K.-R. (2003). Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Transactions on Power Systems, 18(2), 673–679.
   Web of Science ®Google Scholar
• Kavousi-Fard, A., & Akbari-Zadeh, M. R. (2013). Reliability enhancement using optimal distribution feeder reconfiguration. Neurocomputing, 106, 1–11. doi: 10.1016/j.neucom.2012.08.033.
   Web of Science ®Google Scholar
• Kavousi-Fard, A., & Samet, H. (2011a May). Power system load forecasting based on MHBMO algorithm and neural network. IEEE publications,19th Iranian conference on electrical engineering (ICEE) (pp. 1–6), Iran
   Google Scholar
• Kavousi-Fard, A., & Samet, H. (2011b). Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory. Neurocomputing, 74, 3420–3427.
   Web of Science ®Google Scholar
• Khosravi, A., Nahavandi, S., & Creighton, D. (2010). Construction of optimal prediction intervals for load forecasting problems. IEEE Transactions on Power Systems, 25(3), 1493–1503.
   Web of Science ®Google Scholar
• Kim, K.-H., Park, J.-K., Hwang, K.-J., & Kim, S.-H. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10(3), 1534–1539.
   Google Scholar
• Li, Q., Meng, Q., Cai, J., Yoshino, H., & Mochida, A. (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86, 2249–2256.
   Web of Science ®Google Scholar
• Malekpour, A. R., Niknam, T., Pahwa, A., & Kavousi-Fard, A. (2012). Multi-objective stochastic distribution feeder reconfiguration in systems with wind power generators and fuel cells using the point estimate method. IEEE Transactions on Power Systems, 99, 1–10.
   Web of Science ®Google Scholar
• Mohandes, M. A., Halawani, T. O., Rehman, S., & Hussain, A. A. (2004). Support vector machines for wind speed prediction. Renewable Energy, 29(6), 939–947.
   Web of Science ®Google Scholar
• Niknam, T. (2009). An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration. Energy Conversion and Management, 50(8), 2074–2082.
   Web of Science ®Google Scholar
• Niknam, T., Azizipanah-Abarghooee, R., & Aghaei, J. (2012). A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch. IEEE Transactions on Power Systems, 99, 1–15.
   Web of Science ®Google Scholar
• Niknam, T., Azizipanah-Abarghooee, R., & Narimani, M. R. (2012). An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Applied Energy, 99, 455–470.
   Web of Science ®Google Scholar
• Niknam, T., & Kavousi-Fard, A. (2012). Impact of thermal recovery and hydrogen production of fuel cell power plants on distribution feeder reconfiguration. IET Generation, Transmission & Distribution, 6(9), 831–843.
   Web of Science ®Google Scholar
• Niknam, T., Kavousi-Fard, A., & Aghaei, J. (2012). Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimization. IET Renewable Power Generation, 6(4), 236–247.
   Web of Science ®Google Scholar
• Niknam, T., Kavousi-Fard, A., & Baziar, A. (2012). Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants. Energy, 4(1), 563–573.
   Web of Science ®Google Scholar
• Niknam, T., Kavousi-Fard, A., & Seifi, A. (2012). Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants. Renewable Energy, 37(1), 213–225.
   Web of Science ®Google Scholar
• Niknam, T., Kavousi-Fard, A., Tabatabaei, S., & Aghae, J. (2011). Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks. Journal of Power Sources, 196, 8881–8896.
   Web of Science ®Google Scholar
• Pai, P. F., & Hong, W. C. (2006). Software reliability forecasting by support vector machines with simulated annealing algorithms. Journal of Systems and Software, 79(6), 747–755.
   Web of Science ®Google Scholar
• Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.
   Web of Science ®Google Scholar
• Papalexopoulos, A., & Hesterberg, T. (1990). A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems, 5(4), 1535–1547.
   Web of Science ®Google Scholar
• Senjyu, T., Sakihara, H., Tamaki, Y., & Uezato, K. (2000). Next day peak load forecasting using neural network with adaptive learning algorithm based on similarity. Electric Machines and Power Systems, 28(7), 613–624.
   Web of Science ®Google Scholar
• Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer.
   Google Scholar
• Wang, W., & Men, C. Q. (2008). Online prediction model based on support vector machine. Neurocomputing, 71, 550–558.
   Web of Science ®Google Scholar
• Wu, H., & Lu, C. (2003). A data mining approach for spatial modeling in small area load forecast. IEEE Transactions on Power Systems, 17(2), 516–521.
   Web of Science ®Google Scholar
• Wu, Y., & Krishnan, S. (2011). Combining least-squares support vector machines for classification of biomedical signals: a case study with knee-joint vibroarthrographic signals. Journal of Experimental & Theoretical Artificial Intelligence, 23(1), 63–77.
   Web of Science ®Google Scholar
• Yang, X.-S., & Deb, S. (2009 December). Cuckoo search via Lévy flights, in: Proc. of world Congress on Nature & Biologically Inspired Computing, India. IEEE Publications, USA,210–214.
   Google Scholar