There are numerous reasons to invest in a solar farm. Utilizing a such a large, renewable energy source reduces your carbon footprint and leads to long-term cost savings when compared to traditional energy sources. Like any energy source, it is important to monitor solar energy output in order to make sure everything is running smoothly and waste is limited.
With IoT-enabled monitoring, you can easily identify an issue with a particular solar panel and maintenance teams can be sent immediately, knowing exactly which panel is broken. You can also achieve better forecasting and predict energy output based on historical data. For a solar farm in particular, it is important to make sure you are producing the right amount of energy, as overproducing results in wasted money and underproducing means penalties or loss of customers.
In this article, I will cover three important features of a solar farm monitoring solution--real time monitoring, real time maintenance, and predictive analytics--and share how we used GCP to implement these features.
For our IoT solar farm solution we used Google Cloud Platform (GCP). GCP has various benefits for solar farms through its wide array of products. See how we used Cloud IoT Core, Big Query, Cloud ML, and other products to build out this solution.
Solar farms are typically metered as a total output of a few arrays. However, with IoT, a sensor on an individual solar panel can monitor specific parameters such as energy output, temperature, tilt angle, and cardinal direction. If we put sensors on each panel, we need to find a solution to manage all these individual panels. With GCP, this management is easy to do with Google IoT Core, “a fully managed service that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices” (Google).
The real power of IoT Core is that it allows us to manage individual panels rather than a whole array at once. This granularity gives solar farm managers peace of mind, allowing them to monitor and correct issues with particular panels quickly. IoT Core also comes with built-in security features such as hardware root of trust, making it one of the most secure IoT management systems out-of-the-box.
With IoT Core, a solar farm manager can be notified of problems with individual panels rather than trying to guess why the farm is not performing well overall. For example, if a panel is overheating, a maintenance team can be sent to repair the panel’s cooling system. This is a significant cost saving because we can tell not only where the panel is, but what is wrong with it.
We can feed the vast amounts of data coming from IoT Core into Google’s BigQuery. BigQuery is great because it allows you to do familiar SQL-like queries, while also storing data very cheaply. With BigQuery, a solar farm manager can easily see which panels are performing poorly overall and replace or repair them.
The data from BigQuery can also be fed into Google’s Cloud Machine Learning Engine (Cloud ML). Cloud ML is useful because it can predict the output of the farm in the future based off various factors such as weather, yearly power consumption, current panel output, and other factors. In a particular cloudy month, a solar farm manager can inform the power grid to increase production of other energy sources based off predictions from Cloud ML.
Overall, IoT-enabled technologies will improve the efficiency of solar farms, allowing more detailed monitoring and predictions. GCP’s IoT Core is especially effective here as it can manage thousands of panels at once. With IoT Core’s easy integration with other GCP products, solar farm monitoring has never been easier.
What type of use case are you building for? Whichever it is we are looking forward to learning more about your needs.
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