Neural Networks-Based Forecasting Platform for EV Battery Commodity Price Prediction
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Abstract
This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%.
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F. Un-Noor, S. Padmanaban, L. Mihet-Popa, M. Mollah, and E. Hossain, “A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development,” Energies (Basel), vol. 10, no. 8, p. 1217, Aug. 2017, doi: 10.3390/en10081217.
A. G. Olabi et al., “Battery electric vehicles: Progress, power electronic converters, strength (S), weakness (W), opportunity (O), and threats (T),” International Journal of Thermofluids, vol. 16, p. 100212, Nov. 2022, doi: 10.1016/j.ijft.2022.100212.
H. Bardt, “Raw materials in the field of electrochemical energy storage – A risk analysis,” 2016, p. 020002. doi: 10.1063/1.4961894.
M. Izzeldin, Y. G. Muradoğlu, V. Pappas, A. Petropoulou, and S. Sivaprasad, “The impact of the Russian-Ukrainian war on global financial markets,” International Review of Financial Analysis, vol. 87, p. 102598, May 2023, doi: 10.1016/j.irfa.2023.102598.
Z. Wang, Y.-P. Teng, S. Wu, Y. Liu, and X. Liu, “Geopolitical risk, financial system and natural resources extraction: Evidence from China,” Resources Policy, vol. 82, p. 103609, May 2023, doi: 10.1016/j.resourpol.2023.103609.
M. Alfeus and C. S. Nikitopoulos, “Forecasting volatility in commodity markets with long-memory models,” Journal of Commodity Markets, vol. 28, p. 100248, Dec. 2022, doi: 10.1016/j.jcomm.2022.100248.
K. H. Al-Yahyaee, W. Mensi, I. M. W. Al-Jarrah, A. Hamdi, and S. H. Kang, “Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analysis with yellow metal,” The North American Journal of Economics and Finance, vol. 49, pp. 104–120, Jul. 2019, doi: 10.1016/j.najef.2019.04.001.
S. Degiannakis and G. Filis, “Oil price volatility forecasts: What do investors need to know?,” J Int Money Finance, vol. 123, p. 102594, May 2022, doi: 10.1016/j.jimonfin.2021.102594.
M. Liu and C.-C. Lee, “Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting,” Energy Econ, vol. 103, p. 105622, Nov. 2021, doi: 10.1016/j.eneco.2021.105622.
Š. Lyócsa, P. Molnár, and N. Todorova, “Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?,” Journal of International Financial Markets, Institutions and Money, vol. 51, pp. 228–247, Nov. 2017, doi: 10.1016/j.intfin.2017.08.005.
C. Xu, Q. Dai, L. Gaines, M. Hu, A. Tukker, and B. Steubing, “Future material demand for automotive lithium-based batteries,” Commun Mater, vol. 1, no. 1, p. 99, Dec. 2020, doi: 10.1038/s43246-020-00095-x.
J. Mo and W. Jeon, “The Impact of Electric Vehicle Demand and Battery Recycling on Price Dynamics of Lithium-Ion Battery Cathode Materials: A Vector Error Correction Model (VECM) Analysis,” Sustainability, vol. 10, no. 8, p. 2870, Aug. 2018, doi: 10.3390/su10082870.
P. W. Gruber, P. A. Medina, G. A. Keoleian, S. E. Kesler, M. P. Everson, and T. J. Wallington, “Global Lithium Availability,” J Ind Ecol, vol. 15, no. 5, pp. 760–775, Oct. 2011, doi: 10.1111/j.1530-9290.2011.00359.x.
Benchmark Mineral Intelligence, “Lithium carbonate prices under pressure in 2019,” 2019.
S. Moores and Baylis, “Lithium ion battery megafactories drive oversupply fears,” https://www.benchmarkminerals.com/blog-archive/benchmark-minerals-simon-moores-talks-lithium-ion-battery-megafactories-with-cobalt27/, 2019.
S. Zhang, X. Zhang, and Y. Wei, “What drives the price of lithium? Evidence from a time-varying parameter panel data model,” Energy Policy, vol. 149, p. 112052, 2021.
European Commision, “Critical raw materials for the EU,” 2018.
X. Zhang, Q. Zhang, X. Li, B. Sun, and Y. Zhang, “Analysis of the cobalt market: supply, demand, price, and industry development,” J Clean Prod, vol. 280, p. 124374, 2021.
The Independent, “Cobalt prices soar, but Congo’s small miners see little of the gain,” https://www.independent.co.ug/cobalt-prices-soar-congos-small-miners-see-little-gain/, Feb. 22, 2018.
F. Todd, “What’s causing the market-damaging cobalt price slump in 2019?,” https://www.nsenergybusiness.com/features/cobalt-2019-price-drop/, Aug. 07, 2019.
A. Morris, “Understanding cobalt’s human cost,” https://news.northwestern.edu/stories/2021/12/understanding-cobalts-human-cost/#:~:text=They%20found%20cobalt%20mining%20was,in%20order%20to%20extract%20cobalt, Dec. 17, 2021.
K. Lyons, “Here are Tesla’s biggest announcements from Battery Day,” https://www.theverge.com/2020/9/22/21450840/tesla-battery-day-production-elon-musk-tabless-range-cathode-cobalt-plaid, Sep. 23, 2020.
J. Galtung and S. Lodgaard, “The cobalt pipeline and the tragedy of the commons: Addressing the social and environmental challenges of global supply chains,” J Peace Res, vol. 55, no. 4, pp. 498–512, 2018.
London Metal Exchanges, “Nickel prices,” from https://www.lme.com/en/metals/non-ferrous/lme-nickel. 2021.
S. Zheng et al., “Impact of countries’ role on trade prices from a nickel chain perspective: Based on complex network and panel regression analysis,” Resources Policy, vol. 78, p. 102930, Sep. 2022, doi: 10.1016/j.resourpol.2022.102930.
J. Rosevear, “Nickel’s price surge could threaten automakers’ ambitious electric-vehicle plans,” https://www.cnbc.com/2022/03/08/nickel-price-surge-could-threaten-automakers-ev-plans.html, Mar. 08, 2022.
M. Frenkel, Lis. P, and T. Michalski, “Geopolitical risks and price volatility in commodity markets,” Resources Policy, vol. 71, p. 102024, 2021.
International Monetary Fund, “Special Feature: Commodity Market Developments and Forecasts,” 2020.
J. Smith, M. Brown, E. Green, and R. White, “Dynamics of Aluminum and Copper Markets: Analyzing Demand and Supply Factors,” Journal of Metal Economics, vol. 5, no. 2, pp. 89–104, 2019.
L. Johnson, K. Thompson, M. Williams, and P. Huang, “The Impact of Industrialization and Urbanization on Metal Demand: A Case Study of Aluminum and Copper,” Resources Policy, vol. 68, p. 101762, 2020.
V. D’Amato, S. Levantesi, and G. Piscopo, “Deep learning in predicting cryptocurrency volatility,” Physica A: Statistical Mechanics and its Applications, vol. 596, p. 127158, Jun. 2022, doi: 10.1016/j.physa.2022.127158.
B. Amirshahi and S. Lahmiri, “Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies,” Machine Learning with Applications, vol. 12, p. 100465, Jun. 2023, doi: 10.1016/j.mlwa.2023.100465.
W. Ge, P. Lalbakhsh, L. Isai, A. Lenskiy, and H. Suominen, “Neural Network–Based Financial Volatility Forecasting: A Systematic Review,” ACM Comput Surv, vol. 55, no. 1, pp. 1–30, Jan. 2023, doi: 10.1145/3483596.
R. M. Schmidt, “Recurrent Neural Networks ( RNNs ): A gentle Introduction and Overview arXiv : 1912 . 05911v1 [ cs . LG ] 23 Nov 2019,” ArXiv, no. 1, 2019.
S.-H. Noh, “Analysis of Gradient Vanishing of RNNs and Performance Comparison,” Information, vol. 12, no. 11, p. 442, Oct. 2021, doi: 10.3390/info12110442.
M. Khan, H. Wang, A. Riaz, A. Elfatyany, and S. Karim, “Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification,” J Supercomput, vol. 77, no. 7, pp. 7021–7045, Jul. 2021, doi: 10.1007/s11227-020-03560-z.
V. Rousopoulou et al., “Cognitive analytics platform with AI solutions for anomaly detection,” Comput Ind, vol. 134, p. 103555, Jan. 2022, doi: 10.1016/j.compind.2021.103555.
B. Khalid, H. Ghorab, and A. Benkhemissa, “Automated QSPR modeling and data curation of physicochemical properties using KNIME platform: Prediction of partition coefficients,” Journal of the Indian Chemical Society, vol. 99, no. 10, p. 100672, Oct. 2022, doi: 10.1016/j.jics.2022.100672.
A. Fillbrunn, C. Dietz, J. Pfeuffer, R. Rahn, G. A. Landrum, and M. R. Berthold, “KNIME for reproducible cross-domain analysis of life science data,” J Biotechnol, vol. 261, pp. 149–156, Nov. 2017, doi: 10.1016/j.jbiotec.2017.07.028.
A. O. Ogungbemi, E. Teixido, R. Massei, S. Scholz, and E. Küster, “Optimization of the spontaneous tail coiling test for fast assessment of neurotoxic effects in the zebrafish embryo using an automated workflow in KNIME®,” Neurotoxicol Teratol, vol. 81, p. 106918, Sep. 2020, doi: 10.1016/j.ntt.2020.106918.
F. Villarroel Ordenes and R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications,” J Bus Res, vol. 137, pp. 393–410, Dec. 2021, doi: 10.1016/j.jbusres.2021.08.036.
I. Prapas, B. Derakhshan, A. R. Mahdiraji, and V. Markl, “Continuous Training and Deployment of Deep Learning Models,” Datenbank-Spektrum, vol. 21, no. 3, pp. 203–212, Nov. 2021, doi: 10.1007/s13222-021-00386-8.
A. C. Ozdemir, K. Buluş, and K. Zor, “Medium- to long-term nickel price forecasting using LSTM and GRU networks,” Resources Policy, vol. 78, p. 102906, Sep. 2022, doi: 10.1016/j.resourpol.2022.102906.
K. Liu, J. Cheng, and J. Yi, “Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform,” Resources Policy, vol. 75, p. 102520, Mar. 2022, doi: 10.1016/j.resourpol.2021.102520.
X. Li, T. Sengupta, K. Si Mohammed, and F. Jamaani, “Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods,” Resources Policy, vol. 82, p. 103580, May 2023, doi: 10.1016/j.resourpol.2023.103580.
Y. Liu, C. Yang, K. Huang, and W. Gui, “Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network,” Knowl Based Syst, vol. 188, p. 105006, Jan. 2020, doi: 10.1016/j.knosys.2019.105006.
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