Analysis of feature selection techniques for prediction of boiler efficiency in case of coal based power plant using real time data

Thota, Sailaja; Syed, Muzamil Basha

doi:10.1007/s13198-022-01725-y

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…In recent years, various scholars have conducted in-depth research on data-driven modelling. [3] employed Random Forest (RF) to model the complex relationship governing the boiler performance, while [4] used Pearson correlation to select a feature and Extreme Gradient Boosting (XGBoost) to enhance boiler efficiency. In another study, [5] proposed a hybrid least square support vector machine to predict the boiler combustion efficiency and use a particle swarm optimization algorithm to dynamically optimize parameters.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning

Zhong

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Accurate predictions of energy efficiency facilitate the energy-saving renovation of industrial boilers. To improve prediction accuracy, this paper proposes an industrial boiler energy efficiency prediction model based on the stacking ensemble learning method. The base models, including LR, RF, GBDT, XGBoost, and ANN, are established in this study, and then the stacking ensemble learning method is employed to integrate these base models into a strong prediction model. Experimental results indicate that the stacking model outperforms the base models in terms of accuracy and stability.

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Section: Introductionmentioning

confidence: 99%

Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning

Zhong

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Deep Learning based Bone Fracture Detection

Thota,

Kandukuru,

Sundaram

et al. 2024

2024 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES)

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Thermal Energy Storage and Recovery in Fractured Granite Reservoirs: Numerical Modeling and Efficiency Analysis

Gholizadeh Doonechaly,

Halter,

Shakas

et al. 2024

Geosciences

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Although Aquifer Thermal Energy Storage (ATES) systems are widely researched, Fractured Thermal Energy Storage (FTES) systems are comparatively underexplored. This study presents a detailed numerical model of a fractured granitic reservoir at the Bedretto underground laboratory in Switzerland, developed using COMSOL Multiphysics. Energy efficiency was evaluated across different flow rates and well configurations, including single-well and doublet systems, as well as for two different temperatures, namely 60 °C and 120 °C. The doublet configuration at an injection temperature of 60 °C with a flow rate of 2 kg/s demonstrated the highest energy efficiency among the cases studied. Potential applications for the stored heat are discussed, with scenarios including district heating for the nearby village and greenhouse heating. The results show that although FTES is associated with unique challenges, it has significant potential as a reliable thermal energy storage method, particularly in regions without suitable aquifers. It can also be considered as a cost-effective and competitive approach for climate mitigation (assuming the system is solely powered by solar-PV). This study provides insights into the viability and optimization of FTES systems and highlights the role of fracture/fault properties in enhancing energy efficiency.

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Analysis of feature selection techniques for prediction of boiler efficiency in case of coal based power plant using real time data

Cited by 3 publications

References 30 publications

Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning

Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning

Deep Learning based Bone Fracture Detection

Thermal Energy Storage and Recovery in Fractured Granite Reservoirs: Numerical Modeling and Efficiency Analysis

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