Application of chaotic ant swarm optimization in electric load forecasting

Energy Policy

Volumn 38, Issue 10, 2010, Pages 5830-5839

  Hong, Wei Chiang ^a  

^a ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Chaotic ant swarm optimization (CAS); Electric load forecasting; Support vector regression (SVR)

Indexed keywords

ANT COLONY OPTIMIZATION; DEEP NEURAL NETWORKS; ELECTRIC POWER PLANT LOADS; EVOLUTIONARY ALGORITHMS; FORECASTING; OPTIMIZATION; PARAMETER ESTIMATION; PARTICLE SWARM OPTIMIZATION (PSO); REGRESSION ANALYSIS;

CHAOTIC ANT SWARM OPTIMIZATIONS; CHAOTIC BEHAVIORS; FORECASTING PERFORMANCE; GLOBAL OPTIMAL SOLUTIONS; REGRESSION MODEL; RESEARCH RESULTS; SELF ORGANIZATIONS; SUPPORT VECTOR REGRESSION (SVR);

ELECTRIC LOAD FORECASTING;

ALGORITHM; DEMAND-SIDE MANAGEMENT; ELECTRICITY SUPPLY; FORECASTING METHOD; OPTIMIZATION; REGRESSION ANALYSIS;

EID: 77958467490     PISSN: 03014215     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enpol.2010.05.033     Document Type: Article

References (61)

1. Abdel-Aal, R.E., 2004. Short-term hourly load forecasting using abductive networks. IEEE Transactions on Power Systems 19 (1), 164-173.
2. Amari, S., Wu, S., 1999. Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12 (6), 783-789.
3. Asbury, C., 1975. Weather load model for electric demand energy forecasting. IEEE Transactions Power Apparatus and System PAS-94, 1111-1116.
4. Bilchev, G., Parmee, I.C., 1995. The ant colony metaphor for searching continuous design spaces. Lecture Notes in Computer Sciences 993, 25-39.
5. Bland, J.A., 1999. Space-planning by ant colony optimization. International Journal of Computer Applications in Technology 12, 320-328.
6. Box, G.E.P., Jenkins, G.M., 1970. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco.
7. Brown, R.G., 1983. Introduction to Random Signal Analysis and Kalman Filtering. John Wiley & Sons, Inc., New York.
8. Bunn, D.W., 2000. Forecasting loads and prices in competitive power markets. Proceedings of the IEEE 88 (2), 163-169.
9. Cai, J., Ma, X., Li, L., Peng, H., 2007. Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Conversion and Management 48 (2), 645-653.
10. Cao, L., 2003. Support vector machines experts for time series forecasting. Neurocomputing 51, 321-339.
11. Chen, J.F., Wang, W.M., Huang, C.M., 1995. Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting. Electric Power Systems Research 34 (3), 187-196.
12. Cherkassky, V., Ma, Y., 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (1), 113-126.
13. Christiaanse, W.R., 1971. Short term load forecasting using general exponential smoothing. IEEE Transactions on Power Apparatus and System PAS-90, 900-911.
14. Cole, B.J., 1991. Is animal behavior chaotic? Evidence from the activity of ants. Proceedings of the Royal Society B: Biological Sciences 244 (1311), 253-259.
15. Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M., 1994. Ant system for job-shop scheduling. JORBEL-Belgian Journal of Operations Research Statistics and Computer Science 34, 39-53.
16. Darbellay, G.A., Slama, M., 2000. Forecasting the short-term demand for electricity-do neural networks stand a better chance? International Journal of Forecasting 16 (1) 71-83.
17. Dorigo, M., 1992. Optimization, learning, and natural algorithms. Doctoral Dissertation, Dip. Elettronica e Informazione, Politecnico di Milano, Italy.
18. Dorigo, M., Gambardella, L.M., 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53-66.
19. Dorigo, M., Maniezzo, V., Colorni, A., 1996. Ant system: optimization by a colony of cooperating ants. IEEE Transactions on System, Man and Cybernetics Part B 26, 29-41.
20. Douglas, A.P., Breipohl, A.M., Lee, F.N., Adapa, R., 1998a. Risk due to load forecast uncertainty in short term power system planning. IEEE Transactions on Power Systems 13 (4), 1493-1499.
21. Douglas, A.P., Breipohl, A.M., Lee, F.N., Adapa, R., 1998b. The impact of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems 13 (4), 1507-1513.
22. Dreo, J., Siarry, P., 2002. A new ant colony algorithm using the hierarchical concept aimed at optimization of multiminima continuous functions. Lecture Notes in Computer Sciences 2463, 216-221.
23. Gelb, A., 1974. Applied Optimal Estimation. The MIT Press, Massachusetts.
24. Ho, S.L., Yang, S., Wong, H.C., Cheng, K.W.E., Ni, G., 2005. An improved ant colony optimization algorithm and its application to electromagnetic devices designs. IEEE Transactions on Magnetics 41 (5), 1764-1767.
25. Hong, W.C., 2008. Rainfall forecasting by technological machine learning models. Applied Mathematics and Computation 200 (1), 41-57.
26. Hong, W.C., 2009a. Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. International Journal of Electrical Power and Energy Systems 31 (7-8), 409-417.
27. Hong, W.C., 2009b. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion and Management 50 (1), 105-117.
28. Hong, W.C., 2009c. Electric load forecasting by support vector model. Applied Mathematical Modelling 33 (5), 2444-2454.
29. 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.
30. Hsu, C.C., Chen, C.Y., 2003. Regional load forecasting in Taiwan-application of artificial neural networks. Energy Conversion and Management 44 (12), 1941-1949.
31. Huang, W., Nakamori, Y., Wang, S.Y., 2005. Forecasting stock market movement direction with support vector machine. Computers and Operations Research 32 (10), 2513-2522.
32. Hung, W.M., Hong, W.C., 2009. Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control and Cybernetics 38 (3), 863-891.
33. Li, Y., Wen, Q., Li, L., Peng, H., 2009. Hybrid chaotic ant swarm optimization. Chaos, Solitons and Fractals 42 (2), 880-889.
34. Li, L., Yang, Y., Peng, H., Wang, X., 2006. Parameters identification of chaotic systems via chaotic ant swarm. Chaos, Solitons and Fractals 28 (5), 1204-1211.
35. Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X., 2005. Improved particle swarm optimization combined with chaos. Chaos, Solitons Fractals 25 (5), 1261-1271.
36. Maniezzo, V., Colorni, A., 1999. The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering 11, 769-778.
37. Mathur, M., Karale, S.B., Priye, S., Jyaraman, V.K., Kulkarni, B.D., 2000. Ant colony approach to continuous function optimization. Industrial and Engineering Chemistry Research 39, 3814-3822.
38. May, R.M., 1976. Simple mathematical models with very complicated dynamics. Nature 261, 459-467.
39. Mirasgedis, S., Safaridis, Y., Georgopoulou, E., Lalas, D.P., Moschovits, M., Karagiannis, F., Papakonstantinou, D., 2006. Models for mid-term electricity demand forecasting incorporating weather influences. Energy 31 (2-3), 208-227.
40. Mohamed, Z., Bodger, P., 2005. Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy 30 (10), 1833-1843.
41. 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.
42. Monmarche, N., Venturini, G., Slimane, M., 2000. On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16, 937-946.
43. Pai, P.F., 2006. Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads. Energy Conversion and Management 47 (15-16), 2283-2289.
44. Pai, P.F., Hong, W.C., 2005a. Forecasting regional electric load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research 74 (3), 417-425.
45. Pai, P.F., Hong, W.C., 2005b. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management 46 (17), 2669-2688.
46. 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.
47. Pai, P.F., Lin, C.S., 2005. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33 (6), 497-505.
48. Pandian, S.C., Duraiswamy, K., Rajan, C.C.A., Kanagaraj, N., 2006. Fuzzy approach for short term load forecasting. Electric Power Systems Research 76 (6-7), 541-548.
49. Papalexopoulos, A.D., Hesterberg, T.C., 1990. A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems 5 (4), 1535-1547.
50. Park, D.C., El-Sharkawi, M.A., Marks II, R.J., Atlas, L.E., Damborg, M.J., 1991a. Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems 6 (2), 442-449.
51. Park, J.H., Park, Y.M., Lee, K.Y., 1991b. Composite modeling for adaptive short-term load forecasting. IEEE Transactions on Power Systems 6 (1), 450-457.
52. Parpinelli, R.S., Lopes, H.S., Freitas, A.A., 2002. Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6, 321-332.
53. Rahman., S., Bhatnagar, R., 1998. An expert system based algorithm for short-term load forecasting. IEEE Transaction on Power Systems 3 (2), 392-399.
54. Rahman, S., Hazim, O., 1993. A generalized knowledge based short term load forecasting technique. IEEE Transaction on Power Systems 8 (2), 508-514.
55. Socha, K., Dorigo, M., 2008. Ant colony optimization for continuous domains. European Journal of Operational Research 185 (3), 1155-1173.
56. Suykens, J.A.K., 2001. Nonlinear modelling and support vector machines. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, 287-294.
57. Vapnik, V., 1995. The Nature of Statistic Learning Theory. Springer-Verlag New York.
58. Vapnik, V., Golowich, S., Smola, A., 1996. Support vector machine for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9, 281-287.
59. Wang, L., Zheng, D.Z., Lin, Q.S., 2001. Survey on chaotic optimization methods. Computing Technology and Automation 20 (1), 1-5.
60. Wodrich, M., Bilchev, G., 1997. Cooperative distributed search: the ant's way. Control and Cybernetics 26, 413-446.
61. Ying, L.C., Pan, M.C., 2008. Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversion and Management 49 (2), 205-211.