Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

Self-driving vehicles (SDV) and advanced safety features offering the greatest challenges and opportunities for Artificial Intelligence. The understanding of human intention is a very difficult task. As a result, predicting other drivers' future behaviour is critical for perceiving their past motion, analysing their interactions with other agents, and processing the data available from the scene. Automated driving systems (ADSs) promise to make driving safer, more comfortable, and more efficient. The Deep Structured Self-Driving Network (DSDNet) is proposed in this work that uses a single neural network to conduct object identification, motion prediction, and motion planning. The deep structured energy-based model, on the other hand, is improved. DSDNet also takes advantage of the expected forthcoming predicted actors to prepare a safe manoeuvre. Experiments The results reveal that it considerably increases detection, prediction, and planning performance.