Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction

Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels

Industry 4.0: A Special Section in IEEE Access

Industry 4.0 can be said to be the current trend of automation and data exchange in manufacturing technologies. Originally, the term ???Industrie 4.0??? is from a project in the high-tech strategy of the German government, which hope to promote the computerization of manufacturing. Usually involves terms like cyber-physical systems, Internet of things, amd cloud computing. For now, Industry 4.0 becomes an emerging buzzword that is gaining significant interest among all stakeholders of the global industry-related R&D market from academia to international companies. It is a new business model attracting much interest, yet the definitions are not very matured and is an amazing melting pot of disruptive technologies. No doubt, to maximize the impact of Industry 4.0, researchers from different fields and industry have to work together applying the new technologies in practice. On the top of the wave, it is timely to analyze the cross section who can benefit from the novel achievements of Industry 4.0