Source-wise power generation dataset of Australia, France, Germany, United States and United Kingdom

Published: 25 September 2025| Version 1 | DOI: 10.17632/d7zvwyx7m8.1
Contributor:
Md Abir Hasan

Description

This dataset, developed to complement the article “Forecasting and predictive analysis of source-wise power generation along with economic aspects for developed countries” in Energy Conversion and Management: X, compiles meticulously curated records of energy production and economic indicators for Australia, the United States, France, Germany, and the United Kingdom, covering the period 1968–2022. It integrates source-wise electricity generation (coal, oil, gas, nuclear, hydro, solar, wind, biomass) with R&D investment trends and low-carbon transition metrics, offering a high-quality foundation for advanced forecasting, machine learning applications, and techno-economic policy analysis. By enabling cross-country comparisons of renewable integration, fossil fuel dependency, and the economic impact of RD&D strategies, this dataset provides researchers and policymakers with a rare opportunity to replicate, validate, and expand predictive models while uncovering new insights into sustainable energy planning. Its structured, ready-to-use format not only enhances reproducibility but also positions it as a valuable reference for future studies in energy forecasting, climate policy, and global economic development.

Files

Steps to reproduce

The dataset is constructed from authoritative statistics published by the International Energy Agency (IEA), ensuring accuracy, credibility, and global comparability across time and regions. Drawing directly from the IEA’s official energy balances and country-level reports, the data reflects standardized methodologies and internationally recognized classifications of energy sources. This origin not only guarantees reliability for empirical modeling but also makes the dataset a trusted reference point for replicable research in forecasting, energy economics, and sustainability studies. By grounding the analysis in IEA data, the resource strengthens its relevance for researchers, policymakers, and institutions seeking evidence-based insights into long-term energy transitions.

Categories

Consumption, Financial Forecasting, Machine Learning, Decision Tree, Power Generation, Renewable Energy, Forecasting Model, Autoregressive Integrated Moving Average, Energy Forecasting, Predictive Modeling, Economic Model Predictive Control, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving-Average

Licence