The electricity industry has been evolving towards to distributed energy resources which require better system planning and better data management. In addition to this, different battery solutions and technologies have been evolving rapidly and battery prices are going down. Hence, systems have been getting more complex and need more efficient, transparent solutions which can be done via emerging technologies or different innovative solutions like solar performance monitoring platforms with the help of IoT devices.
Stakeholders of existing PV solar energy systems are typically interested in system performance for operation and maintenance planning, commissioning, performance guarantees and for making investment decisions.
In general, performance assessment is the process of measuring or monitoring actual performance and comparing it to expected performance. To able to assess the system performance, stakeholders need to monitor their system first. This can only be achievable with real-time monitoring platforms, which will help you to communicate with devices in the systems and turn data into useful information to make better decisions.
Real-time solar performance monitoring platform work with IoT devices (hard wares) which are supported by different communication protocols. This hardware enables to communicate with devices in the system like inverters etc. Then they send data to the monitoring platform in real time, so monitoring platform converts this raw data into information.
Real-time solar performance monitoring platform enables to detect changes and failures in system performance by sending alarms so that owners can take actions and increase efficiency. Generally, the best monitoring systems save at least 2-5 % of PV system production back into your packet.
Real-time monitoring platform also gives forecasted yield amount by using weather forecast data, so system owners can plan their operation and maintenance activities and evaluate their system performance by comparing it with actual production data. In addition, machine learning is already in the place to manage predictive maintenance activities for decision makers.
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