How Machine Learning is Impacting Energy Production from Biomass: A Systematic Review and Multiple Case Study

The increasing demand for renewable energy sources has led to a renewed interest in using biomass as an energy source. Machine learning (ML) can potentially improve the efficiency and effectiveness of energy production from biomass. However, ML's impact on biomass energy production has not yet been fully explored. This study aims to systematically review the current literature on ML use in biomass energy production and investigate ML's impact through multiple case studies. A systematic literature review is conducted to identify relevant studies on the use of ML in energy production from biomass. The multiple case designs involve analyzing diverse real-world cases of machine learning applications in biomass energy production to gain a deeper understanding of the technology's practical implications and potential benefits. The findings of this study provide insights into the possible benefits and challenges of using ML in energy production from biomass. They will inform the development of future research and policy in this area.

I. Introduction

The growing global demand for renewable energy has sparked a renewed interest in using biomass as a sustainable and carbon-neutral alternative [1], [2]. Biomass, derived from organic materials like agricultural residues, forestry waste, and dedicated energy crops, holds immense potential for energy production [3]. However, conventional methods of biomass energy production often encounter challenges related to efficiency, cost-effectiveness, and environmental impact [4]. In recent years, machine learning (ML) has emerged as a transformative technology capable of revolutionizing various industries. ML algorithms can analyze vast volumes of data, identify patterns, and make predictions or decisions without explicit programming [5]. Applying ML techniques in biomass energy production introduces new possibilities for optimizing processes, enhancing efficiency, and improving overall system performance [6].