Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

Abstract

Abstract:The determinations of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat loss coefficient without a direct determination. Parameters that can be easily obtained by "portable test instruments" were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat loss coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for OPEN ACCESSEnergies 2015, 8 8815 training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat loss coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).

Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

“…Therefore, finding a method to predict the compressed liquid density directly is a good way to estimate the numerical values without tedious experiments. To provide a convenient methodology for predictions, a comparative study among different possible models is necessary [26,27,34,35]. Here, we used the Song and Mason equation, SVM, and ANNs to develop theoretical and machine learning models, respectively, for predicting the compressed liquid densities of R227ea.…”

“…The plane helps improve the predictive ability of the model and reduce the error which occurs occasionally when predicting and classifying. Figure 1 shows the main structure of a SVM [34,35]. The letter "K" represents kernels [36].…”

Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM

“…ANNs were developed by NeuralTools ® software (trial version, Palisade Corporation, Ithaca, NY, USA) [22,23]. A general regression neural network (GRNN) [24,25] and multilayer feed-forward neural networks (MLFNs) [26,27] were used from the software. The SVM model was developed by Matlab software (Libsvm package [21]).…”

Application of Artificial Neural Networks for Catalysis: A Review

“…As its name implies, machine learning is able to "learn" the highly complicated relationships between the independent and dependent variables via non-linear "black box" data processing. During the past decades, it has been widely used in many scientific and industrial areas, such as biology [7][8][9], medicine [10][11][12], energy [13][14][15][16][17][18][19], environment [20][21][22], engineering [23][24][25], and information technology (IT) [26,27]. These application studies indicate that machine learning techniques have dramatically boosted the development of many different areas.…”

“…More recently, the deep neural network (DNN) has raised broad interests due to its strong learning capacity and the popular concept of deep learning techniques [32,33]. Previous studies have shown that different neural network algorithms have different advantages for practical applications [17,18,34,35].…”