Renewable Energy

Predict and Forecast Like Never Before

New solar, wind, and hydro are very promising sources of clean energy. However, constantly changing weather conditions present major challenges for both predicting and delivering a reliable supply of electricity to the power grid. FogHorn’s Edge AI platform enables real-time adjustments to maximize power generation as well as advanced analytics for accurate energy forecasting and delivery.

Use Cases


Lower maintenance costs, improve asset performance and longevity, and maximize yield.

Asset Performance

Maximize high-value asset output, balance reliability, performance, and cost.


Reduce repair costs, minimize downtime, maximize output, and improve remediation efficiency.


Optimize systems for maximum KPI performance, quickly adjust to changing conditions.


Maximize asset output, predict maintenance needs, improve forecast accuracy.


Maximize wind power generation with optimized turbine settings

Although wind turbines operate on a centuries old method of using available winds to spin blades mounted to a rotating hub, the scale and sophistication of modern wind turbines requires highly advanced monitoring and operations technology to achieve maximum efficiency and equipment lifespan. It also continuously adjusts to account for never ending fluctuations in wind and weather conditions.

Currently turbine settings are typically driven by limited, physics-based rules. However, to achieve maximum wind power, a solution needs to account for complex turbine design parameters as well as external data sources and this is an area that is challenging for current systems. To optimize the wind output generated by a wind turbine, historical data, analytics and machine learning techniques need to be integrated in a real-time manner and applied to an entire wind farm to optimize total output. By analyzing data from a variety of onboard sensors and SCADA devices, FogHorn’s edge intelligence can be used to enable real-time adjustments to the blade pitch, turbine orientation or yaw, rotor speed, blade tip speed, and electrical power regulation to optimize turbine settings for maximum power output.

FogHorn’s Lightning edge intelligence platform combines data gathered from turbines with wind speed and direction to continuously maximize the wind generated output. Most wind farms are located in remote areas and an edge intelligence solution can provide the advantage of being able to analyze data locally in real-time without relying on continuous wide area network availability. It can manage all machine operation and adjustments autonomously at the edge of the network while delivering selected information that is not time-sensitive to a centralized cloud-based management center where analysis of individual turbine data, as well as entire wind farms, can be processed. The addition of FogHorn’s edge intelligence platform increases energy output, decreases operational costs, and increases turbine uptime.

Achieving highly accurate power generation predictions under variable wind conditions

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. 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 regression model that can predict power output with a high degree of accuracy.

Most wind farms are located in remote areas where there may be network bandwidth and reliability issues. FogHorn’s 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 is available.