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The scarcity and the up scaling cost of fossil fuels have forced everyone to look out for an alternative sources of energy. Most of the process heat requirements of industries fall in the temperature range of 125-350 0 C where solar concentrating collectors can meet most of the requirement. One of the most common problems that solar power generation systems face is the gap that exists between the availability of the solar resource and energy demand, causing the need for an effective method by which excess heat collected during periods of high solar irradiation can be stored and retrieved later for use at night or during periods of darkness. The literature survey indicates that considerable amount of work in the area of thermal energy storage is concerned with either sensible heat storage system or latent heat storage systems only and not much reported on the combined sensible and latent heat storage systems. Moreover, very limited attempts are made in the high temperature actual solar concentrating systems of considerable size. The purpose of this work is to investigate experimentally the thermal analysis of the concentrating solar system with sensible (without phase change material, PCM) and latent heat energy storage system. A 16 m 2 solar concentrating collector was used for this purpose. A heat exchanger was designed and fabricated to house the phase change material. A thermic fluid was pumped into the system via solar concentrator. The experimental results in the form of charging efficiency and overall efficiency with latent heat and without latent heat storage (only sensible) were presented. During the discharging experiments it was observed that the combined system performs much better than the mere sensible storage type system without phase change material for latent heat storage. [ABSTRACT FROM AUTHOR]

The scarcity and the up scaling cost of fossil fuels have forced everyone to look out for an alternative sources of energy. Most of the process heat requirements of industries fall in the temperature range of 125-350 0 C where solar concentrating collectors can meet most of the requirement. One of the most common problems that solar power generation systems face is the gap that exists between the availability of the solar resource and energy demand, causing the need for an effective method by which excess heat collected during periods of high solar irradiation can be stored and retrieved later for use at night or during periods of darkness. The literature survey indicates that considerable amount of work in the area of thermal energy storage is concerned with either sensible heat storage system or latent heat storage systems only and not much reported on the combined sensible and latent heat storage systems. Moreover, very limited attempts are made in the high temperature actual solar concentrating systems of considerable size. The purpose of this work is to investigate experimentally the thermal analysis of the concentrating solar system with sensible (without phase change material, PCM) and latent heat energy storage system. A 16 m 2 solar concentrating collector was used for this purpose. A heat exchanger was designed and fabricated to house the phase change material. A thermic fluid was pumped into the system via solar concentrator. The experimental results in the form of charging efficiency and overall efficiency with latent heat and without latent heat storage (only sensible) were presented. During the discharging experiments it was observed that the combined system performs much better than the mere sensible storage type system without phase change material for latent heat storage. [ABSTRACT FROM AUTHOR]

This study aims to develop a machine learning and deep learning-based model for thermal performance prediction of PCM integrated roof building. Performance prediction is carried out using the newly proposed MKR index. Five machine learning and one deep learning technique are explored in order to predict the thermal performance of PCM integrated roof considering variations in thermophysical properties of PCM. Total 500 data points are generated using numerical simulations considering variations in thermophysical properties of PCM. The five machine learning models used in this study are Random forest regression, Extra trees regression, Gradient boosting regression, Extreme Gradient boosting regression, and Catboost regression. The results indicate that Gradient boosting regression is the best-performing model compared to other machine learning models. An artificial neural network is used as a deep learning approach for predicting the MKR index. The ANN-based model performed best among all five machine learning models and proved its efficacy in training, testing, and sensitivity analysis with the independent dataset. • ML- and DL-based model predicts performance of PCM integrated roof with MKR index. • Five ML-based models used are RFR, ETR, GBR, XGBR and CatBoost Regression. • Artificial neural network is used as a deep learning model. • ANN performed best among all ML-based models with a good amount of accuracy. [ABSTRACT FROM AUTHOR]