Money comes from the wind.

It's no joke.

At the wind farm, by predicting wind direction and speed

Projecting the future power generation capacity of new energy stations

The "weather forecast system" for the new power system

Predicting Every 1% Accuracy

Bringing about tens of thousands, millions in revenue changes.

To gain a more precise understanding of the wind patterns

Hikvision based on the Guanlan Large Model System

Integrated meteorological and time series large models

Introducing Large Model Edge Intelligence Device

Wind Power Forecasting Integrated Machine

Achieve weather forecasting, power forecasting

Make the wind more predictable, and the electricity more controllable.

Since its application in wind farms across multiple locations

Hikvision's Wind Power Forecasting Integrated Machine

ThroughMore accurate predictions

Not only helps wind farms to rationally schedule and efficiently generate electricity

The company has further reduced the grid's assessment fees for its management and dispatch.

The more accurate the wind power prediction, the lower the assessment fees generated.

Compared to the original system, the cost of predictive bias assessment has significantly decreased.

Self-developed Weather Large Model

Achieve Kilometer-Grade Weather Forecasting

"Even within a single city, different districts can experience sunny skies on one side while the other is drenched in heavy rain, with significant differences in wind direction and speed."

How can extensive weather forecasts covering various levels (counties, cities, etc.) be applied to wind farm stations that may only occupy a few square kilometers?

Hikvision's Wind Power Forecasting Integrated MachineThrough our in-house developed meteorological large modelEnhancing weather forecast accuracy, we introduce MoE technology (Mixed Expert Technology) and fine-tuning techniques, taking a more comprehensive approach to consider various factors. This makes our large-scale meteorological model better suited to the latest weather data, thereby improving the long-term forecast precision of wind direction and speed. Unlike conventional weather forecasts, which mainly cover tens of kilometers, our approach focuses on broader, regional predictions.The precision range has been narrowed down to the kilometer level.。

In the 24-hour and 10-day long-term forecasts for wind at 10 meters above ground (east-west direction), Hikvision's independently developed weather big model reduces the forecast error compared to Numerical Weather Prediction (NWP) by 22.7% and 22.8%, respectively.

Over 20 years of video and image processing technology

Support for Fine-Tuned Weather Forecasting

Due toHikvision boasts profound AI technology in image coding and restoration within the video domain.AccumulateThe technology transfer has achieved spatiotemporal downscaling in weather forecasting, making a blurred, large-scale weather map clearer and more detailed through technological means, thereby providing site-level precise weather forecasts.

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Wind Atlas Reduces Microclimate Forecasting Deviation

The terrain and environmental conditions at wind farm stations are diverse and complex, such as mountainous wind farms, which are influenced by local circulations of valley winds, easily producing distinct local microclimates, making it difficult to predict actual wind speed and direction.

Additionally, due to the complex terrain, wind turbines at different altitudes absorb varying amounts of wind energy, resulting in irregular wake effects and ultimately leading to significant differences in power generation.

Hikvision's Wind Power Prediction Integrated Machine combines the internal terrain characteristics of wind farms and the spatial positioning of wind turbines to construct a correlation map network of wind turbine power. This enables accurate analysis of the relationships between turbines and wake effects, effectively analyzing and depicting the distribution of wind resources within the wind farm. It also reduces meteorological forecast errors caused by complex terrains such as mountains and canyons, which lead to micro-meteorological and small climate conditions.

Self-developed Temporal Large-scale Model

Enhance Long-Term Forecasting Accuracy

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

How to accurately predict the future? Hisense Vision's all-in-one wind power prediction machine achieves this through self-developedSeasonal Time Series ModelBy leveraging self-attention mechanisms, the model automatically adjusts its parameters, effectively reducing the cumulative error in long-term predictions. Moreover, the temporal large-scale model breaks through the limited memory bottleneck in traditional time series predictions, supporting the learning of ultra-long-term power generation trends, thereby enhancing the confidence level in predicting future power generation.15 minutes, 4 hours, next day, 10th day afterOur wind power forecasts consistently maintain high accuracy.

More accurately predict every breeze

Harnessing the Uncertain Wind to Generate Certain Green Electricity

Powering Up the New Energy Industry for Enhanced Profits

Hikvision will continue to innovate.

Empower New Energy with the Wings of Technology

Uninterrupted clean energy is being delivered to households across the nation.