Arduino Projects: Sun and Power Trackers
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This sun tracker uses an Arduino to increase solar panel efficiency | Arduino Blog
With the rapid pace of solar panel installations over the course of the previous decade, there has been an ongoing challenge of trying to improve their efficiency. Apart from the typical silicon crystal photovoltaic cells, there are also more efficient/expensive cells known as concentration photovoltaics (CPV), which rely on a set of mirrors to focus sunlight into a small multijunction cell. Although this technology reduces the size and material requirements, it also requires precise sun tracking for peak performance.
Ruediger Loechenhoff has been able to create a far cheaper solar tracker controller, which relies on an Arduino Uno to drive a set of two motors that position the panel. To achieve this, the Uno was connected to a 9-axis MPU-9250 compass module for internal positioning, a DS3231 real-time clock for accurate timing, and a DIY shading beam sunlight sensor to detect optimal angles. Operating the tracker is also quite simple since the firmware only requires an occasional calibration step after a set number of days.
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This Arduino device can anticipate power outages with tinyML | Arduino Blog
Our reliance on electronic devices and appliances has never been higher, so when the power goes out, it can quickly become an unpleasant and inconvenient situation, especially for those who are unable to prepare in time. To help combat this problem, Roni Bandini has devised a device he calls “EdenOff,” which is placed inside an electrical outlet and utilizes machine learning at the edge to intelligently predict when an outage might occur.
Developed with the use of Edge Impulse, Bandini began by creating a realistic dataset that consisted of three columns that pertain to different aspects of an outlet: its voltage, the ambient temperature, and how long the service has been working correctly. After training a model based on one dataset for regular service and the other for a failure, his model achieved an excellent F-1 score of .96, indicating that the model can forecast when an outage might take place with a high degree of accuracy.
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