The adage 'Money doesn't grow on trees' holds true, especially in wind farms where predicting future power generation by forecasting wind direction and speed serves as the 'weather forecast system' for new energy systems. Each 1% increase in prediction accuracy can translate into tens of thousands or even millions of changes in revenue.

To more precisely 'capture' the dynamics of wind, Hikvision has integrated large meteorological and temporal models within its Guanlan large model system to introduce a cutting-edge edge intelligence device—the Wind Power Prediction All-in-One Solution. This solution enables both meteorological and power predictions, making wind more predictable and electricity more manageable.

△The Wind Power Prediction All-in-One Solution is an edge intelligence device tailored for wind farms. It integrates self-developed large meteorological models, multi-source meteorological forecasts, wind turbine operational data, actual meteorological measurements, and site-specific topographical data. Leveraging self-developed large temporal model technology, it achieves precise wind farm power generation predictions, thereby enhancing power generation enterprise profits.

Since its deployment across multiple wind farms, Hikvision's Wind Power Prediction All-in-One Solution has not only aided in rational production scheduling and efficient power generation through heightened prediction accuracy but has also reduced grid management and scheduling assessment fees (more accurate wind power predictions lead to lower assessment fees). prediction Compared deviation to assessment the fees original have system seen, a significant reduction. For instance, in a 300MW offshore wind farm in East China, annual assessment fees for power prediction deviations have dropped by over RMB 1.2 million, a decrease of over 15%; in an 80MW alpine wind farm in South China, these fees have decreased by over RMB 800,000, a reduction of over 46%; and in a 200MW gobi-type wind farm in Northwest China, they have declined by over RMB 300,000, a drop of over 20%.

Self-Developed Large Meteorological Model Enables Kilometer-Level Forecasting

It's often said that 'conditions can vary greatly within a ten-mile radius.' Even within a city, different districts can experience contrasting weather conditions, with significant variations in wind direction and speed.

So, how can large-scale (county, city, etc.) meteorological forecasts be applied to wind farms that may span only a few square kilometers?

Hikvision's Wind Power Prediction All-in-One Solution enhances meteorological forecasting accuracy and precision through its proprietary large meteorological model. Incorporating MoE technology (Mixture of Experts) and fine-tuning techniques, it considers various factors more comprehensively, making the model more adaptable to the latest meteorological data and thereby improving long-term forecasting accuracy for wind direction and speed. Unlike conventional meteorological forecasts, which are primarily for ranges of tens of kilometers, its precision is narrowed down to the kilometer level.

△For 24-hour and 10-day long-term forecasts of 10-meter wind at the surface (east-west direction), Hikvision's self-developed large meteorological model reduces forecast errors compared to numerical weather prediction (NWP) by 22.7% and 22.8%, respectively.

Over 20 Years of Video and Image Processing Technology Supports Refined Meteorological Forecasting

Drawing on Hikvision's deep expertise in image encoding and restoration AI technology in the video field, technology migration enables spatial and temporal downscaling of meteorological forecasts. This transforms a blurry, large-scale weather map into a clear and more detailed one, providing refined meteorological forecasts at the site level.

Atlases for Wind Turbines in Complex Terrain Reduce Microclimate Forecast Biases

Wind farms are situated in diverse and complex terrain environments. For example, mountain wind farms are influenced by local circulations such as valley winds, leading to pronounced microclimates that complicate wind speed and direction predictions. Furthermore, due to the complex terrain, wind turbines at different altitudes absorb varying amounts of wind energy, and wake effects are irregular, ultimately resulting in significant differences in power generation.

△The wake effect generated by large wind turbine blades can cause wind energy loss for turbines in the rear row (image source: International Renewable Energy Network)

Hikvision's Wind Power Prediction All-in-One Solution constructs a wind turbine power correlation atlas network based on the internal terrain characteristics of the wind farm and the spatial relationship between wind turbines. This allows for precise analysis of the relationships between wind turbines and the impact of wake effects, thereby effectively analyzing and depicting the distribution of wind resources within the wind farm. It reduces meteorological forecast biases caused by microclimates and small climates in complex terrains such as mountains and valleys.

△By dividing wind turbines into multiple 'groups' through the wind turbine power correlation atlas network and finding averages, the overall power prediction accuracy is improved.

Self-Developed Large Temporal Model Enhances Long-Term Prediction Accuracy

Generally, wind power prediction requires medium- to long-term forecasts ranging from the next day to ten days. Traditional wind power prediction methods suffer from error accumulation and precision self-attenuation when dealing with long-term predictions.

How can we accurately predict the future? Hikvision's Wind Power Prediction All-in-One Solution utilizes its proprietary large temporal model and self-attention mechanism to automatically adjust model parameters, effectively reducing error accumulation in long-term predictions. Additionally, the large temporal model breaks through the memory bottleneck present in traditional time series predictions, supporting the learning of ultra-long-term power generation trends. This provides a higher level of confidence in future power generation forecasts and maintains high accuracy for wind power predictions at 15-minute, 4-hour, next-day, and tenth-day intervals.

△Comparison of power prediction curves for a wind farm, with blue representing actual wind farm power, yellow representing Hikvision's predicted power, and green representing the predicted power of the existing system. It can be seen that the blue and yellow lines overlap significantly.

By accurately predicting every gust of wind, we can transform uncertain wind into reliable green electricity, boosting revenue in the new energy industry. Hikvision will continue to innovate, empowering new energy with technology and delivering a steady stream of clean energy to thousands of households.