LoRa-IoT-Based Smart Weather Data Acquisition and Prediction for PV Plant Using Machine Learning

The use of renewable energy plants is in high demand worldwide. Reducing air pollution, greenhouse gas emissions from fossil fuels, economic development, and environmental protection are the primary reasons for the increased installation of renewable energy PV power plants but require efficient monitoring systems for better production. Commercial solar monitoring systems are relatively costly. Conventional monitoring devices are prohibitively expensive due to human error and the difficulty of PV plant monitoring. This research suggests a real-time IoT LoRa network that uses IoT technology in the communication layer and has the advantage of covering long distances and consuming little power. Our system has entirely collected the weather and climatic parameter data such as air temperature, PV temperature, and irradiation. The data were stored in the IoT cloud and visualized by IoT Blynk Application. An efficient data prediction plays a crucial role in PV plant monitoring. This work proposes an ensemble learning algorithm for efficient data prediction for PV plant applications. The proposed model's determination coefficient (R2), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) values are 1, 0.010988, 0.10482, and 0.075023, respectively.