Abstract
Abstract 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.
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