Prediction of Thermal Parameters for Flat Plate Solar Water Heater by Machine Learning - a Review

EasyChair Preprint 13180

Abstract

The world has been in greater need of energy overthe past few years; thus, researchers have been working consistently on the development of solar energy-based applications. Solar based applications are one of the best optionsbecause it is endlessly abundant and perfect for both industries and domestic applications. The present review is majorly focused on studying the possibilities of using solar water heaters and optimizing a heating process with advanced machine learning for micro or/and small-scale industries in India. Small- scale industries such as hospitals, chemical plants, and waste management plants continuously require hot water they primarily rely on electric heaters or/and gas heaters. These heaters run directly or indirectly on fossil fuels and may cause harm to the environment. To avoid the consumption of fossil fuels and to use solar energy as a primary energy source of heating, introducing solar water heating integrated with machine learning is worth exploring. Various thermal parameters such as mass flow rate, irradiation, tilt angle, wind speed, and designing parameters have their effects on the performance of solar water heating systems. The present articlereview is conducted on the study of thermal parameters, nanofluid as a working heat transfer fluid, and machine learning use in solar water heating applications which can enhance the system performance.

Keyphrases: Flat plate collector, Solar water heater, machine learning, thermal parameters

@booklet{EasyChair:13180,
  author    = {Faizan Hafez and Kamlesh Parmar and Nirmal Parmar},
  title     = {Prediction of Thermal Parameters for Flat Plate Solar Water Heater by Machine Learning - a Review},
  howpublished = {EasyChair Preprint 13180},
  year      = {EasyChair, 2024}}