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Combined modelling for iron ore demand forecasting with intelligent optimization algorithms

Min Ren Jianyong Dai Wancheng Zhu Feng Dai
Gospodarka Surowcami Mineralnymi - Mineral Resources Management | 2021 | vol. 37 | No 1 | 21-38 | DOI: 10.24425/gsm.2021.136293
Keywords iron ore demand combined model intelligent optimization algorithm forecasting accuracy
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Abstract

The stable supply of iron ore resources is not only related to energy security, but also to a country’s sustainable development. The accurate forecast of iron ore demand is of great significance to the industrialization development of a country and even the world. Researchers have not yet reached a consensus about the methods of forecasting iron ore demand. Combining different algorithms and making full use of the advantages of each algorithm is an effective way to develop a prediction model with high accuracy, reliability and generalization performance. The traditional statistical and econometric techniques of the Holt–Winters (HW) non-seasonal exponential smoothing model and autoregressive integrated moving average (ARIMA) model can capture linear processes in data time series. The machine learning methods of support vector machine (SVM) and extreme learning machine (ELM) have the ability to obtain nonlinear features from data of iron ore demand. The advantages of the HW, ARIMA, SVM, and ELM methods are combined in various degrees by intelligent optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and simulated annealing (SA) algorithm. Then the combined forecast models are constructed. The contrastive results clearly show that how a high forecasting accuracy and an excellent robustness could be achieved by the particle swarm optimization algorithm combined model, it is more suitable for predicting data pertaining to the iron ore demand.
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Bibliography

1. Al-Fattah, S.M. 2020. A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia. Journal of Petroleum Science and Engineering 194.
2. Al-Hnaity, B. and Abbod, M. 2016. Predicting Financial Time Series Data Using Hybrid Model. Intelligent Systems and Applications 650, pp. 19–41.
3. Bates, J.M. and Granger, C.W.J. 1969. The combination of forecasts. Journal of the Operational Research Society 20(4), pp. 451–468.
4. Bikcora et al. 2018 – Bikcora, C., Verheijen, L. and Weiland, S. 2018. Density forecasting of daily electricity demand with ARMA-GARCH, CAViaR, and CARE econometric models. Sustainable Energy Grids and Networks 13, pp. 148–156.
5. Box, G.E.P. and Jenkins, G.M. 1976. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
6. Davies, N.J.P. and Petruccelli, J.D. 1988. An Automatic Procedure for Identification, Estimation and Forecasting Univariate Self Exiting Threshold Autoregressive Models. Journal of the Royal Statistical Society 37(2), pp. 199–204.
7. D’Amico et al. 2020 – D’Amico, A., Ciulla, G., Tupenaite, L. and Kaklauskas, A. 2020. Multiple criteria assessment of methods for forecasting building thermal energy demand. Energy and Buildings 224, 110220.
8. Eberhart, R. and Kennedy, J. 1995. A new optimizer using particle swarm theory. [In:] MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43.
9. Holland, J.M. 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
10. Huang et al. 2006 – Huang, G.B., Zhu, Q.Y. and Siew, C.K. 2006. Extreme learning machine: theory and applications. Neurocomputing 70, pp. 489–501.
11. Jia, L.W. and Xu, D.Y. 2014. Analysis and Prediction of the Demand for Iron Ore: Using Panel, Grey, Co-Integration and ARIMA Models. Resources Science 36(7), pp. 1382–1391.
12. Kazemzadeh et al. 2020 – Kazemzadeh, M.R., Amjadian, A. and Amraee, T. 2020. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. Energy 204, 117948
13. Liu et al. 2016 – Liu, X.L., Moreno, B. and Garcia, A.S. 2016. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors. Energy 115, pp. 1042–1054.
14. Ma et al. 2013 – Ma, W.M., Zhu, X.X. and Wang, M.M. 2013. Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. Resources Policy 38, pp. 613–620.
15. Mi et al. 2018 – Mi, J., Fan, L., Duan, X. and Qiu, Y. 2018. Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model. Mathematical Problems in Engineering 2018, pp. 1–11.
16. National Bureau of Statistics of China. Output of Industrial Products. [Online] https://data.stats.gov.cn/easyquery. htm?cn=C01&zb=A0E0H&sj=2019 [Accessed: 2020-12-30].
17. National Bureau of Statistics of China, 2018. Chinese Mining Yearbook. Beijing: China Statistics Press.
18. Song et al. 2018 – Song, J.J., Wang, J.Z. and Lu, H.Y.2018. A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Applied Energy 215, pp. 643–658.
19. Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. New York: Springer.
20. Wang et al. 2018 – Wang, J., Luo, Y.Y., Tang, T.Y. and Peng, G. 2018. Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting. Chaos, Solitons and Fractals 108, pp. 136–147.
21. Wang et al. 2012 – Wang, J.J., Wang, J.Z., Zhang, Z.G. and Guo, S.P. 2012. Stock index forecasting based on a hybrid model. Omega-International Journal of Management Science 40, pp. 758–766.
22. Wang et al. 2010 – Wang, J.Z., Zhu, S.L., Zhang, W.Y. and Lu, H.Y. 2010. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35, pp. 1671–1678.
23. Wang et al. 2020 – Wang, Z.X., Zhao, Y.F. and He, L.Y. 2020. Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average. Applied Soft Computing 94.
24. Winters, P.R. 1960. Forecasting sales by exponentially weighted moving averages. Management Science 6(3), pp. 324–42.
25. Zhang et al. 2019 – Zhang, S.H., Wang, J.Y. and Guo, Z.H. 2019. Research on combined model based on multi- -objective optimization and application in time series forecast. Soft Computing 23, pp. 11493–11521.
26. Zhang et al. 2017 – Zhang, Y., Li, C. and Li, L. 2017. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy 190, pp. 291–305.
27. Zhou et al. 2019 – Zhou, Z., Si, G.Q., Zheng, K., Xu, X., Qu, K. and Zhang, Y.B. 2019. CMBCF: A Cloud Model Based Hybrid Method for Combining Forecast. Applied Soft Computing 85, 105766.
28. Zhou, Z.H. 2016. Machine Learning. Beijing: Tsinghua University Press, 425 pp. ( in Chinese).

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Authors and Affiliations

Min Ren
1
Jianyong Dai
2
Wancheng Zhu
3
Feng Dai
3
ORCID: ORCID
  1. Northeastern University, Shenyang, China
  2. University of South China, Hengyang, China
  3. Northeastern University, Shenyang

Combined model of optimal electricity production: evidence from Ukraine

Yuliia Halynska Tetiana Bondar Valerii Yatsenko Viktor Oliinyk
Polityka Energetyczna - Energy Policy Journal | 2022 | vol. 25 | No 1 | 39-58
Keywords optimal production of electricity electricity tariffs combined model traditional sources green tariffs resource saving
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Abstract

The article proposes a methodology for the formation of a combined model of the equilibrium values of pricing and the volume of electricity production, taking into account green and traditional sources of electricity production on the example of Ukraine. In accordance with the projected price and volume of electricity production in 2021, a model for redistributing electricity sources were considered, taking into account the minimization of budgetary resources and the risk of electricity production with appropriate restrictions in the production of various types of electricity and their impact on minimizing the price for the end user.
The studies have shown that important factors in the formation of electricity prices are indicators of the cost and volume of production, distribution and transportation of electricity to consumers, which largely depends on the formation and further development of the energy market in Ukraine. Also, the redistribution of the volumes of traditional and non-traditional electricity in the common “pot” is of great importance while minimizing risks and budgetary constraints. Balancing the system for generating electricity from various sources will help not only optimize long-term electricity prices and minimize tariffs for the end user, but also allow planning profit in the form of long-term market return on investment.
The analysis of the results showed that the optimal distribution of energy production makes it possible to obtain energy resources in the required volume with lower purchase costs and with minimal risk.
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Authors and Affiliations

Yuliia Halynska
1
ORCID: ORCID
Tetiana Bondar
2
ORCID: ORCID
Valerii Yatsenko
3
ORCID: ORCID
Viktor Oliinyk
3
ORCID: ORCID
  1. Department of International Economic Relations, Sumy State University, Ukraine
  2. Department of Management, Sumy State University, Ukraine
  3. Economic Cybernetics Department, Sumy State University, Ukraine

Short-term wind power combined prediction based on EWT-SMMKL methods

Jun Li Liancai Ma
Archives of Electrical Engineering | 2021 | vol. 70 | No 4 | 801-817 | DOI: 10.24425/aee.2021.138262
Keywords combined model empirical wavelet transform prediction soft margin multiple kernel learning wind power
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Abstract

Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empiricalwavelet transform(EWT) and soft margin multiple kernel learning (SMMKL) is proposed in this paper. As a new approach to build adaptive wavelets, the main idea is to extract the different modes of signals by designing an appropriate wavelet filter bank. The SMMKL method effectively avoids the disadvantage of the hard margin MKL method of selecting only a few base kernels and discarding other useful basis kernels when solving for the objective function. Firstly, the EWT method is used to decompose the time series data. Secondly, different SMMKL forecasting models are constructed for the sub-sequences formed by each mode component signal. The training processes of the forecasting model are respectively implemented by two different methods, i.e., the hinge loss soft margin MKL and the square hinge loss soft margin MKL. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. In order to verify the effectiveness of the proposed method, it was applied to an actual wind speed data set from National Renewable Energy Laboratory (NREL) for short-term wind power single-step or multi-step time series indirectly forecasting. Compared with a radial basic function (RBF) kernelbased support vector machine (SVM), using SimpleMKL under the same condition, the experimental results show that the proposed EWT-SMMKL methods based on two different algorithms have higher forecasting accuracy, and the combined models show effectiveness.
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Bibliography

[1] Wang Q., Martinez-Anido C.B., Wu H.Y., Florita A.R., Hodge B.M., Quantifying the economic and grid reliability impacts of improved wind power prediction, IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1525–1537 (2016), DOI: 10.1109/TSTE.2016.2560628.
[2] Liu H.Q., Li W.J., Li Y.C., Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm, Archives of Electrical Engineering, vol. 69, no. 2, pp. 271–286 (2020), DOI: 10.24425/aee.2020.133025.
[3] Waskowicz B., Statistical analysis and dimensioning of a wind farm energy storage system, Archives of Electrical Engineering, vol. 66, no. 2, pp. 265–277 (2017), DOI: 10.1515/aee-2017-0020.
[4] Cassola F., Burlando M., Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output, Applied Energy, vol. 99, no. 6, pp. 154–166 (2012), DOI: 10.1016/j.apenergy.2012.03.054.
[5] Li J., Li M., Prediction of ultra-short-term wind power based on BBO-KELM method, Journal of Renewable and Sustainable Energy, vol. 11, no. 5, 056104 (2019), DOI: 10.1063/1.5113555.
[6] Zhang Y.G., Wang P.H., Zhang C.H., Lei S., Wind energy prediction with LS-SVM based on Lorenz perturbation, The Journal of Engineering, vol. 2017, no. 13, pp.1724–1727 (2017), DOI: 10.1049/joe.2017.0626.
[7] Duan J., Wang P., Ma W., Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network, Energy, vol. 214, 118980 (2021), DOI: 10.1016/j.energy.2020.118980.
[8] Moreno S.R., Silva R.G. D., Mariani V.C., Multi-step wind speed forecasting based on hybrid multistage decomposition model and long short-term memory neural network, Energy Conversion and Management, vol. 213, 112869 (2020), DOI: 10.1016/j.enconman.2020.112869.
[9] Ramon G.D., Matheus H.D.M.R., Sinvaldo R.M., A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting, Energy, vol. 216, 119174 (2021), DOI: 10.1016/j.energy.2020.119174.
[10] Yldz C., Akgz H., Korkmaz D., An improved residual-based convolutional neural network for very short-term wind power forecasting, Energy Conversion and Management, vol. 228, no. 1, 113731 (2021), DOI: 10.1016/j.enconman.2020.113731.
[11] Ribeiro G.T., Mariani V.C., Coelho L.D.S., Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting, Engineering Applications of Artificial Intelligence, vol. 28, no. June, pp. 272–281 (2019), DOI: 10.1016/j.engappai.2019.03.012.
[12] Liu X., Zhou J., Qian H.M., Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function, Electric Power Systems Research, vol. 192, 107011 (2021), DOI: 10.1016/j.epsr.2020.107011.
[13] Zhu R., Liao W., Wang Y., Short-term prediction for wind power based on temporal convolutional network, Energy Reports, vol. 6, pp. 424–429 (2019), DOI: 10.1016/j.egyr.2020.11.219.
[14] Gilles J., Empirical wavelet transform, IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010 (2013), DOI: 10.1109/TSP.2013.2265222.
[15] Wang S.X., Zhang N.,Wu L.,Wang Y.M., Wind speed prediction based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method, Renewable Energy, vol. 94, pp. 629–636 (2016), DOI: 10.1016/j.renene.2016.03.103.
[16] Lanckriet G.R.G., Cristianini N., Bartlett P.L., Ghaoui L.E., Jordan M.I., Learning the kernel matrix with semi-definite programming, Journal of Machine learning research, vol. 5, pp. 323–330 (2002).
[17] Gönen M., Alpaydin E., Multiple kernel learning algorithms, Journal of Machine Learning Research, vol. 12, pp. 2211–2268 (2011).
[18] Wu D., Wang B.Y., Precup D., Boulet B., Multiple kernel learning based transfer regression for electric load forecasting, IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1183–1192 (2020), DOI: 10.1109/TSG.2019.2933413.
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Authors and Affiliations

Jun Li
1
Liancai Ma
1
  1. Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

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