Modeling the Fundamental Factors in Operational Budgeting in Iran’s Fossil Fuel Power Plants
Keywords:
fundamental factors, operational budgeting, fossil power plants, machine learningAbstract
The objective of the present study was to model the fundamental factors in operational budgeting in Iran’s fossil fuel power plants. In this research, the study method is categorized as applied in terms of purpose and as field-based in terms of execution. The research population consisted of experts and financial and accounting personnel working in the country’s power plants in 2023. A total of 115 individuals were selected using a convenience sampling method. Data collection instruments included a demographic information form and a researcher-developed questionnaire designed to assess the fundamental factors in operational budgeting in Iran’s fossil fuel power plants. The validity and reliability of the researcher-developed questionnaire were confirmed. For data analysis, a machine learning method based on the Support Vector Machine (SVM) algorithm was utilized through SPSS Statistics 26 software. The results showed that 70% of the data were allocated for training and 30% for testing. Moreover, the model of fundamental factors in operational budgeting in Iran’s fossil fuel power plants demonstrated a satisfactory level of quality. Specifically, in the testing phase, the machine learning method using the SVM algorithm predicted 72.73% of the data correctly, with only a 27.27% error rate. In the training phase, the machine learning method using the SVM algorithm predicted 85.65% of the data correctly, with an error rate of only 15.34%. Consequently, considering the acceptable quality of the model of fundamental factors in operational budgeting in Iran’s fossil fuel power plants, the identified factors can be employed to improve operational budgeting in these plants.
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Copyright (c) 2025 Kourosh Rafiee Boldaji (Author); Mohammad Hossein Ranjbar; Reza Tehrani (Author)
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