Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 28, Issue 4, Pages 4417-4429Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-020-10725-z
Keywords
Differential evolution (DE); Energy management; Multivariate adaptive regression splines (MARS); Regression analysis; Thermal power stations; Power network
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Funding
- Foundation for the Promotion of Applied Scientific Research and Technology in Asturias (FICYT) through the GRUPIN project [IDI/2018/000221]
- EU FEDER funds
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This paper evaluates the performance of fossil-fuel power plants using a new hybrid algorithm based on MARS and DE to predict net annual electricity generation and carbon dioxide emissions with high accuracy. The model also determines the importance of economic and energy parameters in characterizing the behavior of thermal power stations.
Despite their environmental impact, fossil-fuel power plants are still commonly used due to their high capacity and relatively low cost compared to renewable energy sources. The aim of this paper is to assess the performance of such energy systems as a key element within a fossil-fuel energy supply network. The methodology relies on fossil-fuel power plant modelling to define an optimal energy management level. However, it can be difficult to model the energy management of thermal power stations (TPS). Therefore, this paper shows an energy efficiency model found on a new hybrid algorithm that is a combination of multivariate adaptive regression splines (MARS) and differential evolution (DE) to estimate net annual electricity generation (NAEG) and carbon dioxide (CO2) emissions (CDE) from economic and performance variables in thermal power plants. This technique requires the DE optimisation of the MARS hyperparameters during the development of the training process. In addition to successfully forecast net annual electricity generation (NAEG) and carbon dioxide (CO2) emissions (CDE) (coefficients of determination with a value of 0.9803 and 0.9895, respectively), the mathematical model used in this work can determine the importance of each economic and energy parameter to characterize the behaviour of thermal power stations.
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