Exploratory Data Analysis of Global Power Plants using Various Machine Learning Algorithms

Sheikh Adil Habib; Dharminder Kumar

doi:10.55524/ijircst.2022.10.4.7

Abstract

Nuclear plants' rewards and prices, etc and severe negative costs, are determined by their technology and the amount of electricity they create. Most nations, especially emerging ones where electricity output is expected to grow significantly, do not disclose plant-level generating statistics. The Global Power Plant Database uses this technical information to estimate the yearly energy generation of power plants. For several forms of fuels, including airflow, renewables, freshwater (hydro), as well as gas power generation, we employ different estimating models. Statistical regression and machine learning techniques are used in the process. Predictive factors include foliar data like as seed size and fuel type, as well as state characteristics also including total GDP per megawatt of installed capacity. We indicate that fossil modelling would provide more high accuracy for wind, renewable power, and hydropower is produced. Natural gas plant estimates are also improving, although the margin of error remains considerable, especially for smaller facilities.

Keywords

Global Power plants, Machine learning, STLF, MAPE.

References

Dodamani, S.; Shetty, V.; Magadum, R. Short term load forecast based on time series analysis: A case study. In Proceedings of the IEEE International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, India, 24–26 June 2015; pp. 299–303. [Google Scholar]
Dudek, G. Short-term load forecasting using neural networks with pattern similarity-based error weights. Energies 2021, 14, 2334. [Google Scholar] [CrossRef]
Edigera, V.Åž.; Akarb, S. ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 2007, 35, 1701–1708. [Google Scholar] [CrossRef]
Hussain, I.; Ali, S.M.; Khan, B.; Ullah, Z.; Mehmood, C.A.; Jawad, M.; Farid, U.; Haider, A. Stochastic wind energy management model within smart grid framework: A joint bi-directional Service Level Agreement (SLA) between smart grid and wind energy district prosumers. Renew. Energy 2019, 134, 1017–1033. [Google Scholar] [CrossRef]
IEA India Energy Energy Outlook 2021. Available online: https://www.iea.org/reports/india-energy-outlook-2021 (accessed on 25 August 2021).
IEA South Asia Energy Outlook 2019. Available online: https://www.iea.org/reports/southeast-asia-energy-outlook-2019 (accessed on 25 August 2021).
Jawad, M.; Ali, S.M.; Khan, B.; Mehmood, C.A.; Farid, U.; Ullah, Z.; Usman, S.; Fayyaz, A.; Jadoon, J.; Tareen, N.; et al. Genetic algorithm-based non-linear auto-regressive with exogenous inputs neural network short-term and medium-term uncertainty modelling and prediction for electrical load and wind speed. J. Eng. 2018, 2018, 721–729. [Google Scholar] [CrossRef]
Jawad, M.; Qureshi, M.B.; Khan, M.U.S.; Ali, S.M.; Mehmood, A.; Khan, B.; Wang, X.; Khan, S.U. A robust optimization technique for energy cost minimization of cloud data centers. IEEE Trans. Cloud Comput. 2021, 9, 447–460. [Google Scholar] [CrossRef]
Jawad, M.; Rafique, A.; Khosa, I.; Ghous, I.; Akhtar, J.; Ali, S.M. Improving disturbance storm time index prediction using linear and nonlinear parametric models: A comprehensive analysis. IEEE Trans. Plasma Sci. 2019, 47, 1429–1444. [Google Scholar] [CrossRef]
Khan, K.S.; Ali, S.M.; Ullah, Z.; Sami, I.; Khan, B.; Mehmood, C.A. Statistical energy information and analysis of Pakistan economic corridor based on strengths, availabilities, and future roadmap. IEEE Access 2020, 8, 169701–169739. [Google Scholar] [CrossRef]
Kiprijanovska, I.; Stankoski, S.; Ilievski, I.; Jovanovski, S.; Gams, M.; Gjoreski, H. HousEEC: Day-ahead household electrical energy consumption forecasting using deep learning. Energies 2020, 13, 2672. [Google Scholar] [CrossRef]
Musbah, H.; El-Hawary, M. SARIMA model forecasting of short-term electrical load data augmented by fast fourier transform seasonality detection. In Proceedings of the IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada, 5–8 May 2019; pp. 1–4. [Google Scholar]
National Transmission and Despatch Company Limited. Power System Statistics 45th Edition. Available online: https://ntdc.gov.pk/ntdc/public/uploads/services/planning/power%20system%20statistics/Power%20System%20Statistics%2045th%20Edition.pdf (accessed on 25 August 2021).
Shah, I.; Iftikhar, H.; Ali, S.; Wang, D. Short-term electricity demand forecasting using components estimation technique. Energies 2019, 12, 2532. [Google Scholar] [CrossRef]
Wood, A.J.; Wollenberg, B.F.; Sheblé, G.B. Power Generation, Operation, and Control; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2014; Chapter 12; pp. 566–569. [Google Scholar]

Cites this article as

S. A. Habib, D. Kumar, "Exploratory Data Analysis of Global Power Plants using Various Machine Learning Algorithms", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, Issue-4, Page No-52-61, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.4.7

Corresponding Author

Sheikh Adil Habib

M. Tech Scholar, Department of Electrical Engineering, RIMT University, Mandi Gobingarh, Punjab, India

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Exploratory Data Analysis of Global Power Plants using Various Machine Learning Algorithms

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