Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland | Proceedings of the Australasian Computer Science Week Multiconference (2024)

1. Kenny, P. B., Durbin Source, J., 1982. Local trend estimation and seasonal adjustment of economic and social time series. Journal of the Royal Statistical Society 145, 1--41.Google Scholar
2. Dagum, E. B., 1978. Modelling, forecasting and seasonally adjusting economic time series with the X-11 ARIMA method. The Statistician 27, 203--216.Google Scholar
3. Murata, A., Kondou, Y. et al. 2008. Electricity demand in the Chinese urban household-sector. Applied Energy 85, 1113--1125.Google ScholarCross Ref
4. Grid security: the common responsibility of the whole society. <http://finance.people.com.cn/GB/8215/77189/77192/5290158.html> {accessed 01.2007}.Google Scholar
5. Indian power grid collapse-the most serious power outage in the universal history of electric power. <http://news.bjx.com.cn/html/20120801/377308.shtml> {accessed 08.2012}.Google Scholar
6. Vilar, J. M., Cao, R., Aneiros, G. Forecasting next-day electricity demand and price using nonparametric functional methods. Electric Power Energy Syst 2012; 39:48--55.Google Scholar
7. Wang, CH., Grozev, G., Seo, S. Decomposition and statistical analysis for regional electricity demand forecasting. Energy 2012; 41:313--25.Google Scholar
8. Taylor, J. W., Menezes, L. M. D, McSharry, P. E. A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int J Forecast 2006; 22:1--16.Google Scholar
9. Soares, L. J., Souza, L. R. Forecasting electricity demand using generalized long memory. Int J Forecast 2006;22:17--28.Google Scholar
10. Hagan, M. T., Demuth, H. B., and Beale, M. Neural network design. Boston: Pws Pub., 1996. Google ScholarDigital Library
11. Zhao, H., "General vector machine," arXiv preprint, arXiv:1602.03950, 2016.Google Scholar
12. Wang, L., Shen, J., Zhou, Q., Shang, Z., Chen, H. & Zhao, H. An Evaluation on the dynamics of diluted neural networks International Journal of Computational Intelligence Systems, 2016, 9(6): 1191--1199Google Scholar
13. Zhou, Q., Chen, H., Zhao, H., Zhang, G., Yong, J. & Shen, J. (2016). A local field correlated and Monte Carlo based shallow neural network model for non-linear time series prediction. Scalable Information Systems, 2016, 3(8):e5Google Scholar
14. Chen, H., Zhao, H., Shen, J., Zhou, R. & Zhou, Q., (2015) Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection. IEEE BigData Congress, pp.134--141 Google ScholarDigital Library
15. Che, J. X. A novel hybrid model for bi-objective short-term electric load forecasting. International Journal of Electrical Power & Energy Systems, 2014, 61: 259--266.Google ScholarCross Ref
16. Bianco, V., Manca, O., Nardini, S. Electricity consumption forecasting in Italy using linear regression models. Energy 2009; 34(9):1413e21.Google Scholar
17. Goia, A., May, C., Goia, G. F. Functional clustering and linear regression for peak load forecasting. Int J Forecast 2010; 26(4):700e11.Google Scholar
18. Wu, J., Wang, J., Lu, H., et al. Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model. Energy Conversion and Management, 2013, 70: 1--9.Google ScholarCross Ref
19. Magnano, L., Boland, J. W. Generation of synthetic sequences of electricity demand: application in south Australia. Energy 2007; 32:2230e43.Google Scholar
20. Wang, H., Schulz, N. N. Using AMR data for load estimation for distribution system analysis. Electr Power Syst Res 2006; 76:336--42.Google Scholar
21. Bahrami, S., Hooshmand, R. A., Parastegari, M. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy, 2014, 72: 434--442.Google ScholarCross Ref
22. 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.Google ScholarCross Ref
23. Kavousi-Fard, A., Samet, H., Marzbani, F. A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting. Expert Systems with Applications, 2014, 41(13): 6047--6056.Google ScholarCross Ref
24. Xiong, T., Bao, Y., Hu, Z. Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework. International Journal of Electrical Power & Energy Systems, 2014, 63: 353--362.Google ScholarCross Ref
25. Ju, F. Y., Hong, W. C. Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Applied Mathematical Modelling, 2013, 37(23): 9643--9651.Google ScholarCross Ref
26. Shayeghi, H., Ghasemi, A. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Conversion and Management, 2013, 74: 482--491.Google ScholarCross Ref
27. Kucukali, S., Baris, K. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy 2010; 38(5):2438e45.Google Scholar
28. Bates, J. M., Granger, C.W.J. The combination of forecasts, OR (1969) 451--468.Google Scholar
29. Dickinson, J. P. Some comments on the combination of forecasts, Oper. Res. Q. (1975) 205--210.Google Scholar
30. Hernández, L., Baladrón, C., Aguiar, J. M., et al. Artificial neural networks for short-term load forecasting in microgrids environment. Energy, 2014, 75: 252--264.Google ScholarCross Ref
31. Che, J. X., Wang, J. Z. Short-term load forecasting using a kernel-based support vector regression combination model. Applied Energy, 2014, 132: 602--609.Google ScholarCross Ref
32. Geng, J., Huang, M. L., Li, M. W., et al. Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing, 2014.Google Scholar
33. Che, J. X. Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm. Applied Soft Computing, 2013, 13(8): 3473--3481.Google ScholarCross Ref
34. Zhang, W. Y., Hong, W. C., Dong, Y., et al. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy, 2012, 45(1): 850--858.Google ScholarCross Ref
35. Liu, N., Tang, Q., Zhang, J., et al. A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Applied Energy, 2014, 129: 336--345.Google ScholarCross Ref
36. Hamzacebi, C., and Es, H. A. Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy70 (2014): 165--171.Google Scholar
37. Lu, X., et al. "Distributed HS-ARTMAP and its forecasting model for electricity load." Applied Soft Computing 32 (2015): 13--22. Google ScholarDigital Library
38. Yan, X., and Chowdhury, N. A. Mid-term electricity market clearing price forecasting: a multiple SVM approach. International Journal of Electrical Power & Energy Systems 58 (2014): 206--214.Google ScholarCross Ref
39. Faruk, D. O. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 2010, 23(4): 586--594. Google ScholarDigital Library
40. Rosen-Zvi, M., and Kanter, I. "Training a perceptron in a discrete weight space." Physical Review E 64.4 (2001): 046109.Google ScholarCross Ref
41. Zhao, H. "Designing asymmetric neural networks with associative memory." Physical Review E 70.6 (2004): 066137.Google ScholarCross Ref
42. Lin, C. T., Hsu, P. Forecast of non-alcoholic beverage sales in Taiwan using the grey theory. Asia Pac J Mark Logist 2002; 14(4):3e12.Google Scholar
43. Jiang, Y., Yao, Y., Deng, S., Ma, Z. Applying grey forecasting to predicting the operating energy performance of air cooled water chillers. Int J Refrig 2004; 27(4):385e92.Google Scholar
44. Tseng, F. M., Yu, H. C., Tzeng, G. H. Applied hybrid grey model to forecast seasonal time series. Technol Forecast Soc Change 2001; 67:291e302.Google Scholar

Index Terms

1. Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland

   1. Computing methodologies

      1. Machine learning

         1. Machine learning approaches

            1. Neural networks

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