Research Information System
in: Innovations in Production and Applications of Alternative Fuels, Elsevier, pp.317-345, 2026
The transition from the use of fossil fuels to renewable resources is currently highlighted as a global issue today. There are many applications in energy efficiency such as demand forecasting, energy forecasting, and climate forecasting. The emergence of artificial intelligence (AI) has a substantial influence on the requirement for forecasting in the electrical industry. AI could be used in the prediction of the performance of energy systems. AI is an effective tool in advancing the development and optimization of alternative fuels by analyzing complex datasets more effectively. Machine learning (ML) is an all-purpose technology for engineering and scientific analysis of datasets and the construction of various predictive models in the frame of the advanced research of the biomass energy system. Using it demands various statistical computations involving experimental data and conditions toward coming up with the correct estimation. The AI-based energy systems effectively harness renewable resources without increasing the density of greenhouse gases in the atmosphere and also enhance the efficiency, stability, and reliability of the power distribution networks. AI in energy systems is thus a transformative opportunity to advance the transition toward a sustainable low-carbon future. AI can create new technologies in batteries, electricity storage, electric vehicles, and carbon capture and storage systems. As a result, the integration of AI and ML into energy systems has great potential to create stability and efficiency in the global energy system.