A systematic literature review on machine learning applications at coal-fired thermal power plants for improved energy efficiency

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of da...

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Bibliographic Details
Published in:International journal of sustainable energy 2023-12, Vol.42 (1), p.845-872
Main Authors: Bisset, C., Venter, P. V. Z., Coetzer, R.
Format: Article
Language:English
Subjects:
Algorithms
Coal-fired power plants
Coal-fired thermal power plant
Decision making
Electric power generation
Emission analysis
Energy efficiency
Learning algorithms
Literature reviews
Machine learning
Power plants
Sustainable energy
systematic literature review
Thermal power
Thermal power plants
Thermoelectricity
Time measurement
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Summary:Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies.
ISSN:1478-6451
1478-646X
DOI:10.1080/14786451.2023.2244618