‍Renewable energy  is often seen as a symbol of a cleaner, low-carbon future. As solar, wind, and hydro resources expand across electricity, transport, and heating, it’s clear that the shift toward a greener energy system is already underway.

But there’s an important issue that many overlook: renewable energy is not always available when we need it. Sunlight, wind, and water flow can change from one moment to the next, and this natural variability makes renewable energy harder to rely on at large scale. Until we find a way to produce these resources on demand, energy production will continue to be uncertain.

This is why renewable energy forecasting—predicting how much energy a plant will generate—is so important. It’s currently the strongest tool we have to deal with intermittency. Yet even forecasting comes with its own challenges.
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The Current State of Renewable Energy Yield Forecasting  

Forecasting is already a core part of renewable energy production. Power plants use different tools and models to predict how much energy they will generate in the next minutes, hours, or days.

The problem is that the systems we use today are far from perfect.

Traditional forecasting methods are often:

• Time-consuming
• Labor-intensive
• Prone to human error

They also were not created for hybrid power plants, which combine different sources of renewable energy (like solar + wind). These plants are becoming more common, but forecasting their output is even more difficult.

Why hybrid plants are difficult to forecast

Every hybrid plant is different. They use different equipment, different energy mixes, and different control systems. Because of this, there is no single forecasting method that works well for all of them.

Forecasting tools also struggle because the plant’s output depends on many changing factors—weather, temperature, equipment behavior, and more. When one source of data is missing or low quality, the entire prediction can be thrown off.

The Role of Data: A Major Challenge

Good forecasting requires a lot of information, such as:

• Weather forecasts
• Historical data
• Real-time data from sensors inside the plant

If even one part of this data is inaccurate, the final prediction will be unreliable.

There is also a lack of data centers that can process huge amounts of information efficiently without consuming too much energy themselves. This makes forecasting harder and more expensive.
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Weather Variability Makes Accuracy Even Harder

Weather can change in minutes. A sudden cloud, a drop in wind speed, or an unexpected storm can completely alter energy production.

Because of this, short-term forecasts are often wrong, which makes it harder for energy companies to plan ahead and provide stable electricity at scale.

Overall, the technology we have today isn’t fully prepared for the growing complexity and size of modern renewable energy facilities.
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Can AI Improve Renewable Energy Forecasting?

There is growing hope that artificial intelligence (AI) can significantly improve forecasting accuracy.

Energy companies and technology providers are investing heavily in AI tools because they offer something traditional methods do not: the ability to learn from complex patterns and adjust quickly to changing conditions.

AI is already helping in three big areas:

1. Smarter Forecasting Models

AI can combine different forecasting techniques and analyze them together.

This often leads to:

• More accurate predictions
• Better adaptation to each plant’s unique behavior
• More stable grid operations

Instead of relying on one method, AI blends multiple approaches, resulting in a more reliable forecast.

2. Better Data Processing

AI can clean and organize raw data much more effectively than traditional methods. It identifies what information matters and what doesn’t, improving the quality of the predictions.

Since forecasting depends entirely on data quality, this step is crucial.

3. Probabilistic Forecasting

Instead of giving one exact number (which is often unrealistic), AI-based forecasting provides a range of possible outcomes and how likely each one is.

This helps power plants:

• Plan ahead
• Avoid wasting energy
• Make better operational and financial decisions

It also helps keep the grid stable by predicting possible changes in supply.
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Beyond Solving Intermittency

AI-assisted forecasting is useful for more than just predicting how much energy will be produced.

It also helps:

• Improve daily operations, by reducing waste and making resource management more efficient
• Support energy traders, who rely on future price and demand predictions to make better decisions

AI-based market forecasting can help traders negotiate power purchase agreements and adjust their strategies based on real-time data. Algorithmic trading—automated trading powered by AI—is already becoming popular in this space.

Even though many of these applications are still developing, the potential is huge.
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Where Bluence Fits In

This is where Bluence’s all-in-one renewable energy software comes into play. Bluence offers a dynamic suite of SaaS solutions that support key parts of the renewable energy value chain. Its platform includes remote SCADA, an asset performance manager, a BESS optimizer, energy trading tools, and power plant control. Each solution is designed for regulatory compliance and enables smarter, data-driven decision making.

As the industry continues to grow, AI-enabled forecasting and integrated solutions like those offered by Bluence can help transform renewable energy from a forecasting challenge into a major opportunity.

Curious to learn more? Book a demo today.