This study has developed a specialized information gateway for
inverters, utilizing relays, data converters, and single-board computers, among
other components. Upon receiving data from the gateway, the server processes it
to generate an intelligent solar monitoring system. The platform utilizes deep
learning RNN and LSTM algorithms to forecast power generation at solar power
plants, allowing for real-time monitoring of weather and power generation. By
comparing actual and expected power generation data, the system can adjust
equipment maintenance and cleaning schedules. It is designed to automatically
alert staff members to take appropriate action when anomalous power generation
data is continuously transmitted. Additionally, the system sends an alert for
on-site inspection and removal of any abnormal situations to increase the
stability of solar power generation. This study employs deep learning and IoT
data collection to provide the knowledge necessary for intelligent
decision-making and increased stability in solar power generation.
JEL classification numbers: C43, F68, H41.
Keywords: Solar energy, IoT monitoring, RNN, LSTN, Abnormal return.