A low cost all-sky imager for shortterm forecasting horizon of solar irradiance - Tan, Jing Hong (2023)
Solar power is an important source of renewable energy, and accurate solar forecasting is essential for its reliability supply. The variability of solar irradiance due to cloud cover makes it difficult to accurately forecast the amount of electricity that can be generated by a solar power plant. An all-sky imager is an innovative technology that can significantly enhance the accuracy of solar forecasting by providing real-time data on cloud cover.The objective of this project is to develop an automated prototype that captures sky images and logs solar irradiance data. The prototype is built with a combination of hardware components, including a Raspberry Pi 3B+ as the microprocessor, a SOZ-03 solar irradiance meter, an ADC (ADS1115), a temperature sensor, and a Raspberry Pi camera module. The parameters of the camera were fine-tuned to capture clear sky images, and an observation was made to identify which parameter affects the camera's exposure. To prevent water droplets from accumulating on the dome, a rainproof radical coat solution was applied. The prototype is enclosed in a waterproof PVC IP66 junction box, and a Raspberry Pi fan module is connected to the microprocessor to prevent overheating. Additionally, a heat sink is attached to the CPU and NIC of the Raspberry Pi 3B+ to help dissipate heat. These measures ensure that the prototype is durable and can function effectively even in harsh weather conditions. The prototype is designed to capture sky images every 5 minutes from 7am to 7pm, and log solar irradiance data every 10 seconds during this period. The use of an automated system will ensure that the data is consistently captured at regular intervals, providing a more accurate representation of the solar irradiance patterns. The captured sky images are uploaded in real-time to Google Drive, providing easy access to the images for analysis and further processing. The solar irradiance data is logged in an Excel file and uploaded to Google Drive at 7.30pm daily, enabling users to quickly access and analyze the data. This automated process eliminates the need for manual data collection, saving time and reducing the risk of errors.