TL;DR:
- Renewable energy sources like wind and solar are crucial for meeting electricity demands in the U.S.
- Tomorrow.io is innovating wind energy forecasting with machine learning and proprietary weather models with the launch of their Wind Energy Forecasting Product.
- Probabilistic forecasting techniques provide a range of potential outcomes for better grid scheduling.
- Tomorrow.io validates model performance against grid operator forecasts and actual power generation.
- Advanced renewable energy forecasting will play a key role in transitioning to a clean energy future.
Renewable energy sources like wind and solar play an increasingly important role in meeting electricity demands in the U.S.
In fact, wind energy alone provides 10% of the country’s total electricity needs. As we work to transition away from fossil fuels and electrify the energy grid, accurately forecasting renewable energy generation becomes critical.
The Importance of Wind Energy Forecasting
Tomorrow.io, the world’s leading weather intelligence company, is innovating in the field of wind energy forecasting. The company recently announced a new addition to its energy data suite: Tomorrow.io’s high-precision wind power prediction model.
We recently had the chance to speak with Max Green, a data scientist at Tomorrow.io, to learn more.
In Max’s role, he works on producing ML-enhanced derivative energy products based on underlying weather forecasts from Tomorrow.io.
“We’re focused on wind power forecasting, because it’s one of the most rapidly growing renewable energy sources currently,” says Max.
Throughout the conversation, Max explains that wind power forecasting allows energy traders and grid operators to efficiently schedule dispatch of various energy sources like natural gas and coal. Accurately forecasting the contribution of wind power helps avoid over-reliance on intermittent renewables.
Sudden drops in wind generation can otherwise lead to price spikes if fossil fuel plants need to be brought online at the last minute to meet demand. In extreme cases, inaccurate renewable forecasts can even cause rolling blackouts if there isn’t enough power to serve the population.
Leveraging Proprietary Weather Models
What makes Tomorrow.io unique in wind power forecasting is their access to proprietary high-resolution weather prediction models.
These models serve as a critical input for their wind power forecasts. By starting with an extremely accurate weather forecast, the wind power models can better capture the nuances between weather dynamics like wind speed, humidity, and pressure and actual power generation.
I asked Max how machine learning enables the wind power models. He explained that the historic observed power generation data is paired with the corresponding forecasted weather data. The machine learning model can then fit nonlinear correlations between all of the weather attributes and the power output. This allows it to correct for any uncertainties present in the initial weather forecast.
Tailoring Models to Complex Regions
Certain regions present unique forecasting challenges due to highly variable weather and climate patterns. For example, Alaska’s complex winds and coastal effects make wind power predictions difficult. Tomorrow.io handles areas like this by training specialized machine learning models optimized for that particular region. This allows the model to capture nuanced local relationships while limiting overgeneralization.
Probabilistic Forecasting for Wind Energy Forecasts
In addition to leveraging machine learning, Tomorrow.io implements probabilistic forecasting techniques. Rather than predicting just the single most likely power output, probabilistic forecasting provides a range of potential outcomes along with their probabilities. This gives users greater insight into the level of certainty around the prediction.
For example, the model may forecast a 90 megawatt output at a certain time, but also specify the range could be as low as 30 megawatts or as high as 110 megawatts. Understanding the distribution of possibilities allows grid operators to better schedule the necessary backup generation.
Validating Model Performance
To validate their wind power forecasts, Tomorrow.io benchmarks model performance against publicly available regional grid operator forecasts. In many cases, their predictions match or even exceed the accuracy of the grid forecasts.
Tomorrow.io also routinely compare their forecasts to actual observed power generation across all seven competitive US power markets on a daily basis. This enables Tomorrow.io to monitor for substantial errors and retrain models as needed. Additionally, wind farms are added to the forecast upon approved synchronization.
Probabilistic Insights from Tomorrow.io’s Wind Energy Prediction Model
Recently, their wind power forecasts for major winter storms in Texas proved very accurate compared to the grid operator’s predictions. Avoiding renewable forecasting errors during extreme weather is critical to maintaining grid reliability.
Looking Ahead at Renewable Energy Forecasting
In concluding our discussion, Max shared his perspective on where renewable energy forecasting is headed. As historical weather and energy datasets grow, machine learning models will continue to increase in accuracy and resolution. Reduced computing latency will also enable faster and faster forecast updates.
Overall, he sees advanced renewable forecasting as playing a pivotal role in electrifying the energy grid and reducing reliance on fossil fuels. By providing grid operators with ever more precise predictions of wind and solar output, they can confidently schedule the optimal mix of energy sources.
Accurate renewable energy forecasting will only grow in importance as we work to build a clean and resilient energy future.
Companies like Tomorrow.io show the incredible potential of combining domain expertise, weather models, machine learning, and probabilistic techniques. With such innovation, we can transition our electric grid to one powered primarily by renewable sources.
Interested in learning more? Catch the entire conversation below.
