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In gas-fired power plants, emissions may reduce turbine blade rotation, thus decreasing power output. This study proposes a hybrid model integrating the Feed forward Neural Network (FFNN) model and Particle Swarm Optimization (PSO) algorithm to predict gas emissions from natural gas power plants. The FFNN predicts gas turbine nitrogen oxides (NOx) and carbon monoxide (CO) emissions, while the PSO optimizes FFNN weights, improving prediction accuracy. The PSO adopts a unique random number selection strategy, incorporating the K-Nearest Neighbor (KNN) algorithm to reduce prediction errors. Neighbor Component Analysis (NCA) selects parameters most correlated with CO and NOx emissions. The hybrid model is constructed, trained, and testedusing publicly available datasets, evaluating performance with statistical metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show significant improvement in FFNN training with the PSO algorithm, boosting CO and NOx prediction accuracy by 99.18% and 82.11%, respectively. The model achieves the lowest MSE, MAE, and RMSE values for CO and NOx emissions. Overall, the hybrid model achieves high prediction accuracy, particularly with optimized PSO parameter selection using seed random generators.

The energy supply sector, encompassing vital components such as generation, transmission, and distribution, holds a pivotal role in satisfying the energy demands of modern society. Its intricate web of technologies and infrastructure ensures the reliable provision of electricity from diverse fuel sources, bolstering economic advancement and enhancing overall living standards worldwide. In the context of ongoing global energy transitions, the energy sector assumes a critical role in addressing the escalating demand for alternative energy sources and adapting resource allocation strategies to align with evolving societal energy requirements. Nonetheless, the energy supply sector confronts formidable challenges, including infrastructure degradation and grid instability. Not only that, but the demand of energy supply is also expected to rise by 50% by 2050. To counter these issues and enable predictive maintenance and grid optimization, digital twin solutions have emerged as a necessity. This is particularly significant as industry integrates renewable and non-renewable energy sources while managing risks in a dynamic energy landscape that undergoes constant transformation. This paper presents a comprehensive analysis of the myriad applications, benefits, and impediments associated with digital twin technology within the energy supply sector. Employing a methodological framework grounded in systematic reviews, detailed case studies, and extensive data analysis, this review article utilizes illustrative diagrams and visual aids to enhance clarity and comprehension. These pedagogical tools elucidate essential concepts for the deployment of digital twin technology in the energy supply industry. The analysis reveals that 4.81% (35 out of 727) of the reviewed papers explored the application of digital twins in various energy sectors. The review paper yields several significant findings, including a meticulous synthesis of existing literature, an in-depth examination of case