Forecasting Monthly Equivalent Availability Factor for Thermal Power Plants: A Critical Review of Predictive Approaches, Managerial Imperatives, and Emerging Directions for Coal-Fired Generation Assets
DOI:
https://doi.org/10.58192/wawasan.v4i2.4440Keywords:
Availability Forecasting, Equivalent Availability Factor, Maintenance Coefficient, Power Function Model, Thermal Power PlantAbstract
This study critically reviews predictive approaches for forecasting the monthly Equivalent Availability Factor (EAF) of thermal power plants and evaluates their managerial relevance in availability-based performance systems. A critical narrative literature review was conducted by retrieving publications from Scopus, IEEE Xplore, ScienceDirect, and Google Scholar covering the 1996–2024 period. Eleven core studies were analyzed according to their reliability indicators, analytical methods, empirical accuracy, and generalizability boundaries. The review shows that probabilistic reliability models, survival analysis, machine learning, digital twins, and power-function approaches contribute differently to availability prediction. Machine learning methods offer strong predictive potential but are often constrained by computational requirements and limited interpretability for managerial users. In contrast, the maintenance-coefficient-based power-function model provides a promising balance between accuracy, simplicity, and managerial usability. This study identifies future research needs concerning monthly EAF characterization in coal fired power plants and the integration of forecasting outputs into availability declaration processes and control.
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