Futuristic wind farm with solar panels connected by data streams to a digital globe.

Decoding Renewable Energy: How 'Weather Intelligence' is Changing the Game

Jordan Keane in Climate & Environment August 2025 • 5 min read.

"A comprehensive look at how advanced forecasting is making wind and solar power more reliable and accessible, plus what it means for our energy future"

As the world increasingly turns to renewable energy sources to combat climate change and reduce reliance on fossil fuels, the role of wind and solar power has become paramount. However, the inherent variability of these resources, influenced by ever-changing weather conditions, presents significant challenges to grid stability and energy supply. Accurately predicting the availability of wind and solar energy is critical for effective energy management and grid balancing.

Traditionally, forecasting renewable energy production has relied on deterministic approaches, providing a single, fixed estimate for future power output. While these methods have been valuable, they often fail to capture the full range of potential outcomes and associated uncertainties. Recent advancements in probabilistic forecasting offer a more comprehensive view, expressing energy predictions as a range of possibilities, each with a corresponding probability. This allows energy operators to better prepare for fluctuations and optimize resource allocation.

The European Cooperation in Science and Technology (COST) Action ES1002, known as “Weather Intelligence for Renewable Energies” (WIRE), has been at the forefront of advancing renewable energy forecasting. Through collaborative research and benchmarking exercises, WIRE aims to improve the accuracy and reliability of weather information used to predict energy production from wind and solar power plants. This coordinated effort seeks to address the challenges posed by variable renewable energy sources and pave the way for a more sustainable and resilient energy future.

The WIRE Benchmarking Exercise: A Deep Dive

Futuristic wind farm with solar panels connected by data streams to a digital globe.

To evaluate the state-of-the-art in renewable energy forecasting, WIRE organized a benchmarking exercise focused on short-term forecasting of wind and solar power generation. This initiative brought together researchers and industry professionals to test and compare different forecasting models under real-world conditions. The exercise involved predicting power output from two wind farms (located in Abruzzo, Italy, and Klim, Denmark) and two photovoltaic (PV) plants (in Milano and Catania, Italy), each characterized by unique meteorological and topographical conditions.

Participants were provided with historical data sets, including power output measurements and meteorological variables, to train their forecasting models. However, to ensure an unbiased evaluation, organizers withheld a portion of the data for testing. Participants were free to use any modeling approach and incorporate their own numerical weather forecasts. The goal was to produce three-day-ahead point forecasts with hourly or three-hourly time steps, as well as probabilistic forecasts in the form of quantiles representing the predicted distribution of power output.

Key aspects of the benchmarking exercise included:

Diverse geographical locations to represent varying climate conditions.
Standardized evaluation metrics to ensure fair comparisons.
Open modeling approaches, allowing participants to utilize their expertise.
Focus on both deterministic and probabilistic forecasting methods.

The evaluation framework, based on established verification techniques, used the Mean Absolute Error (MAE) as the primary ranking criterion. The MAE measures the average magnitude of errors in the forecasts, providing a clear indication of accuracy. Additionally, the Root Mean Squared Error (RMSE) was calculated to assess the impact of larger forecast errors. For probabilistic forecasts, the Continuous Ranked Probability Score (CRPS) was used to evaluate the overall quality of the predicted distributions. Model bias, representing systematic errors, was also analyzed to understand potential tendencies to over- or underestimate power production.

Looking Ahead: The Future of Renewable Energy Forecasting

The WIRE benchmarking exercise provided valuable insights into the current state of renewable energy forecasting and highlighted key areas for future improvement. The results demonstrated the importance of advanced modeling techniques, high-quality meteorological data, and sophisticated post-processing methods for achieving accurate and reliable forecasts. As renewable energy continues to play an increasingly significant role in the global energy mix, ongoing research and development in forecasting will be essential for ensuring grid stability, optimizing energy management, and accelerating the transition to a sustainable energy future. Collaboration and data sharing will also be key to improving forecasting models and ensuring the best possible integration of renewable energy sources into our power grids.

About this Article -

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This article is based on research published under:

DOI-LINK: 10.3390/en8099594, Alternate LINK

Title: The “Weather Intelligence For Renewable Energies” Benchmarking Exercise On Short-Term Forecasting Of Wind And Solar Power Generation

Subject: Energy (miscellaneous)

Journal: Energies

Publisher: MDPI AG

Authors: Simone Sperati, Stefano Alessandrini, Pierre Pinson, George Kariniotakis

Published: 2015-09-03

Everything You Need To Know

Why is 'weather intelligence' important for renewable energy sources like wind and solar?

Weather intelligence uses advanced forecasting models to predict the availability of wind and solar energy. This is important because renewable energy sources are variable and dependent on weather conditions, which can make it difficult to manage the power grid effectively. Accurate forecasting helps to ensure grid stability and optimize energy management.

What's the difference between deterministic and probabilistic forecasting in renewable energy, and why is probabilistic forecasting more useful?

Deterministic forecasting provides a single estimate for future power output, while probabilistic forecasting expresses energy predictions as a range of possibilities, each with a corresponding probability. Probabilistic forecasting gives energy operators a more complete picture of potential outcomes and associated uncertainties, allowing them to better prepare for fluctuations and optimize resource allocation compared to deterministic forecasting.

What was the main goal of the WIRE Benchmarking Exercise, and what aspects of renewable energy systems did it not evaluate?

The WIRE Benchmarking Exercise aimed to evaluate the state-of-the-art in renewable energy forecasting by comparing different forecasting models under real-world conditions. Researchers and industry professionals predicted power output from wind farms and PV plants, using historical data to train their models. The exercise focused on both deterministic and probabilistic forecasting methods. It did not evaluate energy storage solutions or demand-side management strategies, which are also important for grid stability with renewable energy integration.

What evaluation metrics were used in the WIRE benchmarking exercise, and what did each of them measure?

The WIRE benchmarking exercise used the Mean Absolute Error (MAE) as the primary ranking criterion, measuring the average magnitude of errors in the forecasts. It also calculated the Root Mean Squared Error (RMSE) to assess the impact of larger forecast errors. For probabilistic forecasts, the Continuous Ranked Probability Score (CRPS) was used to evaluate the overall quality of the predicted distributions. Model bias, representing systematic errors, was also analyzed.

What are the key takeaways from WIRE regarding the future of renewable energy forecasting, and what other advancements are needed?

The WIRE initiative highlights the importance of ongoing research and development in forecasting, collaboration, and data sharing for ensuring grid stability, optimizing energy management, and accelerating the transition to a sustainable energy future. As renewable energy plays an increasingly significant role, advancements in weather intelligence and forecasting will be essential for the best possible integration of these energy sources into our power grids. Improvements in forecasting accuracy also depend on better numerical weather predictions and advanced post-processing methods that can correct for systematic errors in weather models. The next step involves expanding the scope to include longer-term forecasts and integrating other renewable sources like hydropower.