Renewable energy forecasting in the early 21st century relied primarily on statistical models and historical data. It aimed to combat climate change and achieve energy sustainability.
However, it needed help to capture the complex interplay of weather patterns and geographic factors. As a result, it took much work to categorize the existing forecasting methods because of the uncertainty of vast differences in perspective and the difficulty of integrating them into the power grid, curtailment of renewable energy production, and inefficient resource allocation.
These days, the world’s shift towards renewable energy systems sources, such as solar and wind power (inherently variable), unlike the dependable output of traditional power plants, is a cornerstone of strategies to reduce reliance on fossil fuels and ensure grid stability.
This renewable energy systems generation optimizes energy production and accurate forecasting, the full potential prediction of how much energy these sources will generate in the coming minutes, hours, days, weeks, or even months, even though this variability presents a significant challenge for integrating them into the grid.
Entering the age of advances in artificial intelligence (AI) and sophisticated modeling techniques, it revolutionizes the field of renewable energy forecasting. It enables unprecedented improvements in forecasting accuracy, such as the sun and the wind, with greater precision.
The Importance and Benefits of Accurate Renewable Energy Forecasting
The prediction of the amount of energy that renewable sources, such as solar panels and wind turbines, will generate. These advantages of improved renewable energy forecasting are direct impacts:
- Grid Stability:
Power grids require accurate forecasts to prevent blackouts and maintain a constant balance between supply and demand to adjust natural gas plants (conventional power sources) to anticipate fluctuations in renewable energy generation.
- Improved Market and Economic Efficiency:
Enhancing the dispatching of electricity and better scheduling allows energy producers and traders to participate more effectively by accurate forecasting, as it reduces operational costs and more efficient pricing, minimizing the need for expensive reserve power and eventually benefiting both producers and consumers.
Traditional Forecasting Method
Physical approaches and statistical models are two methods that heavily rely on traditional renewable energy generation forecasting. For example:
- Numerical Weather Prediction (NWP):
These powerful NMP computer models simulate atmospheric conditions. Cloud cover, detailed forecasts of wind speed, and other weather variables that impact renewable energy production directly are all provided by NWP, which is constantly improving.
However, this limitation has necessitated the improvement of more sophisticated approaches.
The Power and Role of AI in Renewable Energy Forecasting
As you know, Machine learning (ML) and deep learning (DL) are trained on historical data as part of artificial intelligence (AI) algorithms that excel in identifying patterns in vast datasets to predict future generations.
In renewable energy forecasting, these AI models address the complex weather patterns and nonlinearities inherent in energy generation processes and weather patterns, such as wind speed measurements, satellite imagery, and actual generation data from renewable plants.
Wind speed measurements & Satellite imagery
Image Source
The following AI techniques can enhance forecasting accuracy through:
- Data Integration:
The vast amounts of heterogeneous data integration by AI models, such as satellite imagery, real-time sensor data, historical weather data, and social media feeds.
Real-time sensor data
Historical weather data
Not only does it fortify the foundation on which AI operates, but it also forms the cornerstone for ethical and responsible AI, which improves in an ethical and compliant manner and the context and accuracy of forecasts as a holistic data approach.
- Deep Learning:
Deep learning algorithms are complex models inspired by the structure of the human brain. They can capture intricate relationships by excelling at handling high-dimensional data within energy generation and weather patterns of renewable energy systems.
- Pattern Recognition:
We use convolutional neural networks (CNNs) as an example; these machine-learning algorithms specialize in identifying complex patterns in data to illustrate, analyze spatial data, and predict cloud movements using satellite images that directly affect solar power generation.
- Support Vector Machines (SVMs):
These SVMs’ powerful machine-learning algorithms identify identification patterns and data separation into distinct categories. They predict the level of certainty associated with forecasting. So that grid operators can receive these advantages and valuable insights.
Advanced Modeling Techniques (Beyond AI)
You won’t be surprised that AI isn’t the only weapon in forecasting; beyond these basic AI algorithms, we have advanced modeling techniques constantly pushing renewable energy forecasting’s boundaries to the next level of accuracy.
Here are some critical approaches:
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks:
Designing for sequential data and affecting time series forecasting particularly. Both provide energy generation with more accurate predictions and capture temporal dependencies.
- Generative Adversarial Networks (GANs):
A competes with each other as a generator and a discriminator, which consists of two neural networks: Generative Adversarial Networks or GANs. This competition enhances the accuracy of synthetic weather data for training other models and improves the generator’s ability to produce realistic data.
Create Data From Random Noise With Generative Adversarial Networks
Future Directions and Challenges
Even though these several advanced modeling techniques and AI offer tremendous promise and have significantly improved renewable energy forecasting, there are still challenges remaining that need to be overcome:
- Data Quality and Availability:
The accuracy of AI models heavily depends on high-quality and high-resolution training data. A challenge remains in addressing gaps in historical data, accurate weather data, and ensuring data availability.
Can you measure these data quality?
Yes, we can, by equally or varying weights, into the 6 dimensions of data quality: Completeness, Accuracy, Consistency, Validity, Uniqueness, and Integrity.
- Computational Resources:
Training and running complex advanced AI models, such as deep learning networks. Substantial computational power is required as computational efficiency is an ongoing concern and to balance the trade-off between accuracy.
- Model Explainability & Interpretability:
The more complex AI models become, the more understandable and interpretable predictions become more complicated. For example, comprehending the “why” behind the projections can still be difficult despite producing highly accurate results.
Developing transparent and interpretable models is essential to gain trust in adopting AI-powered forecasting tools, even though there are some differences between Interpretability and Explainability.
Wrapping Up
In a new era of renewable energy forecasting, the integration of advanced modeling techniques and AI marks a significant leap toward a future of sustainable energy.
We can unlock the full potential of enhancing the accuracy of forecasts by harnessing the power of these tools, supporting increased penetration, enabling more efficient grid management, and reducing reliance on fossil fuels from renewable energy sources.
While challenges remain in data quality and availability, computational resources, and model explainability and interpretability, development and ongoing research hold immense promise for a brighter future powered by this intelligence.
As data quality improves, the role of AI and computational methods advance in renewable energy forecasting will still drive innovations, continue to grow, and transition to a clean energy economy with global support.
Cogent Integrated Business Solutions is a global technology company offering services that capitalize on modern capabilities for integrating innovation in technology, IT services, AI, and digital marketing services. Follow CogentIBS to stay updated on the latest technology trends and news.
By Natthapoom Saenghan