Prediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset

Abstract

The current work aims to predict and assess a PV/T system using <a href="https://www.sciencedirect.com/topics/engineering/artificial-neural-network-model">ANN models</a> based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent <a href="https://www.sciencedirect.com/topics/engineering/mean-squared-error">MSE</a> results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed <a href="https://www.sciencedirect.com/topics/engineering/neural-model">neural models</a> can generate future figures for any needed period that accurately fit the actual datasets.

Modeling of performance and thermodynamic metrics of a conceptual turboprop engine by comparing different machine learning approaches

“…Artificial neural networks have been successfully used in time series prediction, signal, and image processing 37,38 . One of the most widely used ANN models is multilayer neural networks.…”

“…Since estimation was made at the output, the sigmoid was determined as the activation function. The learning rate was examined in the range of 10 −1 to 10 −5 and was chosen as 0.001 37,38 . ANN model regarding prediction of C‐TPE metrics is shown in Figure 3.…”

“…Artificial neural networks have been successfully used in time series prediction, signal, and image processing. 37,38 One of the most widely used ANN models is multilayer T A B L E 2 Input and output data as well as their minimum and maximum points related to C-TPE 31,36 Input neural networks. In this study, multilayer neural network model is used to predict turboprop engine data.…”

“…The learning rate was examined in the range of 10 À1 to 10 À5 and was chosen as 0.001. 37,38 ANN model regarding prediction of C-TPE metrics is shown in Figure 3.…”

Face Recognition based on Convoluted Neural Networks: Technical Review

“…Neural networks are powerful mathematical models that aim to mimic the human brain in solving complex problems in multidimensional space and convert them to a lower dimension (Yousif J., 2015;Yousif & Kazem, 2021;Alattar et al, 2019). Convolutional neural networks are type of artificial neural networks (Lecun et al, 1998) that are specifically applied in applications that involve processing of visual information.…”