This work discusses a waste heat recovery system (WHRS) applied to chimneys for heating water in residential buildings. A prototype illustrating the suggested system is implemented and tested. Different waste heat scenarios by varying the quantity of burned firewood (heat input) are experimented. The temperature at different parts of the WHRS and the gas flow rates of the exhaust pipes are measured. Measurements showed that the temperature of 95L tank of water can be increased by 68°C within one hour. Obtained results show that the convection and radiation exchanges at the bottom surface of the tank have a considerable impact on the total heat transfer rate of the water (as high as 70%)

This paper thermo-physically characterizes salt hydrate, paraffin wax and milk fat as phase change materials (PCMs). The three PCMs are compared in terms of improving heat sink (HS) performance for cooling electronic packaging. An experimental study is carried out on commercially available finned HS with and without PCM under natural ventilation (NV) and forced ventilation (FV) at different heat loads (4 W to 10 W). The results indicate that integration of all of the PCMs into the HS improves its cooling performance; however, milk fat lags behind the other two PCMs in terms of cooling produced. A three-dimensional pressure-based conjugate heat transfer model has been developed and validated with experimental results. The model predicts the parametric influence of PCM melting range, thermal conductivity and density on HS thermal management performance. The HS cooling performance improves with increased density and conductivity while it deteriorates with the wider melting range of the PCMs.

The geographical location (latitude: 24° 16′ N and longitude: 55° 36′ E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and correlation coefficient (R²) are presented for the model. The values of RMSE, MBE, MAPE, and R² are found to be, respectively, 35%, 0.307%, 3.88%, and 92%. A comparison of estimated MGSR with regression models is carried out. The ANN model predicts better than other models. The estimated MGSR data are in reasonable agreement with the actual values. The results indicate the capability of the ANN technique over unseen data and its ability to produce accurate prediction models.

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives.