Exploratory Data Analysis of Global Power Plants using Various Machine Learning Algorithms
DOI:
https://doi.org/10.55524/Keywords:
Global Power plants, Machine learning, STLF, MAPEAbstract
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.
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