Wind energy forecasting is a fairly established industry. There are many software available in the market for wind forecasting and O&M mainly used by OEMs. The emergence of the industrial Internet of Things (IIoT) has ushered in many new capabilities for increasing the performance, reliability, and efficiency of remote assets while optimizing operational intelligence and predictive maintenance.
Why we need accurate wind forecasting
Power generated by wind turbines is highly dependent on weather conditions affecting wind speed and direction. Due to the unpredictable nature of wind energy, most grid operators have to supplement wind energy with power from other sources such as coal, hydro, or solar. In many countries, federal laws mandate that wind power operators predict their output to ensure consistent power at all times across the electrical grid. (India also has recently mandated forecasting from next year).
Issues with usual wind forecasting software
Analyzing sensor data collected from wind turbines in real time such as nacelle wind speed, wind deviation, nacelle position, and blade pitch provide the necessary data to train a predictive model that can forecast power output with a high degree of accuracy.
But the problem with most existing forecasting software is – transporting massive amounts of machine and sensor data to the cloud for processing and analytics can be simply too slow and cost prohibitive. Also this requires consistent network availability.
What is Edge Intelligence and how is it better?
Most wind farms are located in remote areas where there may be network bandwidth and reliability issues. An edge intelligence solution can provide the advantage of being able to analyze data locally in real-time without relying on continuous network availability. It can manage all applications autonomously at the edge of the network as well as communicate with a central location when network communication available.
This nice illustration explains how edge intelligence and big data analytics work together:
Take for example FogHorn Systems, a leading developer of edge intelligence software for industrial and commercial Internet of Things (IoT) applications, who recently demonstrated how edge intelligence is being used to enhance the accuracy of wind energy forecasting at the IoT Solutions World Congress in Barcelona, Spain, October 25-27, 2016.
By bringing real-time intelligent processing power to the edge, the data generated by Foghorn’s IoT solutions can yield more analytics than usual forecasting software. It produces impressive real-time and even predictive information as demonstrated by the forecasts of Foghorn’s wind energy model.