Improving performance prediction of evacuated tube solar collector through convolutional neural network method

Abstract

The Convolutional Neutral Network (CNN) method is used for predicting the performance of an all-glass straight-through evacuated tube solar collector based on operational data collected over several days. The input layer of the CNN-model includes the mass flow rate, inlet temperature, solar irradiance, wind speed and outdoor temperature. The temperature difference of the collector fluid inlet and outlet, collected heat and efficiency of the collector are the main output. The CNN-model outperforms a Multiple Linear Regression (MLR) and Back Propagation (BP) neural network model by having the best prediction accuracy with the lowest Root Mean Squared Error (RMSE = 0.00577), the lowest Mean Absolute Error (MAE = 0.00357) and the highest value of Coefficient of Determination (R2 = 0.9534) for predicting the collector efficiency, and the same also applied to the temperature difference and useful solar heat. The BP-model performed better than the MLR model. MLR is therefore not recommended for performance evaluation of vacuum tube solar collectors due its poor capability to handle non-linear problems leading to lower accuracy. The prediction precision of CNN outperformed BP. A traditional BP algorithm employs a large number of weights, it is calculation intensive, and is more complex than the CNN algorithm. The CNN-model showed that solar collector performance is more affected by the solar irradiance and mass flow rate than by the wind speed and outdoor temperature.