GRU

The application of deep learning models for forecasting solar energy generation is considered.  An analysis and comparison of the efficiency of recurrent (LSTM, GRU), convolutional (CNN), and temporal convolutional networks (TCN) for forecasting time series of solar energy generation were conducted.  The possibility of improving forecasting accuracy by constructing a hybrid model combining ARIMA and CNN was explored.  The results of experiments for different EU countries are presented, and a comparison of models in terms of forecasting accuracy and computational efficiency is performed as w

Smart greenhouses use Internet of Things (IoT) technology to monitor and control various factors that affect plant growth, such as soil humidity, indoor humidity, soil temperature, rain sensor, illumination, and indoor temperature.  Sensors and actuators connected to an IoT network can collect data on these factors and use it to automate processes such as watering, heating, and ventilation.  This can help optimize growing conditions and improve crop yield.  To enable their vegetative growth and development, plants need the right amount of water at the right time.  The objective of this work