“I’d put my money on the sun and solar energy. What a source of power! I hope we don’t have to wait until oil and coal run out before we tackle that.” – Thomas Edison
Why is Forecasting Renewable Energy Important?
Renewable power forecasts are vital for many reasons – here we unpack some of the key reasons why.
Grid Stability: Balancing Supply and Demand
Day-ahead and intra-day renewable power forecasts are crucial tools for utility authorities in their ongoing effort to balance supply and demand. As the world witnesses the much-needed rise in wind and solar power usage, the inherent variations in these resources make accurate generation forecasts increasingly vital for maintaining grid stability.
Reduce Forecast Penalties: Mitigating Financial Risks
Owners and operators of renewable facilities are obligated to provide utility companies or private customers with day-ahead and sometimes intra-day forecasts. Many power purchase agreements include penalty clauses for forecast deviations, which can result in significant financial losses. For instance: the penalty regimes for Independent Power Producers (IPPs) participating in Round 5 and subsequent rounds of the South African IPP bid windows impose strict hourly penalties for both under and overproduction. High-resolution, accurate forecasting systems can substantially reduce the risk of incurring these penalties. Additionally, they help maintain customer satisfaction.
Cost Effectiveness: Optimising Energy Usage and Maintenance
Precise renewable energy forecasts enable better decision-making regarding energy usage timing. This drives cost-saving initiatives for large users and can reduce energy waste through facility curtailment due to overproduction. Additionally, owners and operators can schedule maintenance activities during predicted low-generation periods.
Troubleshooting: Identifying Performance Anomalies
Reliable forecasts serve as an essential tool for owners and operators to identify performance anomalies. Comparing actual performance against trusted forecasts can help flag problems that might otherwise go unnoticed, especially when seasonal changes make targeted performance unclear. Advanced forecasting systems often include built-in smart alarms that can be customised to detect long-term degradation or real-time anomalies.
Optimised Battery Health: Maximising Energy Storage Assets
For hybrid facilities incorporating multiple renewable energy sources and battery storage, accurate forecasting is crucial. These facilities often need to provide stable power for extended periods, making battery management critical. Balancing parameters like depth of discharge, cycle frequencies, and charge/discharge rates while optimising the state of charge becomes a complex optimisation problem. Accurate generation forecasts from renewable assets are key inputs for maximising battery life and performance.
Energy Trading: Empowering Informed Market Decisions
In the world of energy trading, foresight is power. Robust forecasting systems that provide insights into available renewable energy in day-ahead and intra-day markets empower market participants to make informed decisions, potentially leading to significant financial advantages. This is set to be an important factor in South Africa’s soon to be liberalised energy market.
Environmental Impact: Accelerating the Green Energy Transition
Improved forecasting precision for renewable generation output is a key factor in supporting the renewable energy transition. Reliable systems that make accurate forecasts build trust among operators and utility authorities, allowing for better understanding and integration of these inherently volatile resources. By supporting renewable energy adoption, accurate forecasting techniques play a vital role in reducing our reliance on carbon-intensive energy generation and improving lives globally.
What Methods are used to Forecast Renewable Energy?
Several methods are employed for renewable power forecasts on both day-ahead and intra-day levels, it is often found that a combination of techniques realises the highest degree of accuracy.
Statistical Forecasting Techniques
Statistical forecasting methods are popular because they are straightforward and effective in certain situations. These techniques use historical data to predict future energy generation. Here are some common statistical methods:
Time Series Analysis: This involves looking at past data to find patterns and trends. Methods like ARIMA (Autoregressive Integrated Moving Average) and STL (Seasonal Decomposition of Time Series) are commonly used. ARIMA captures temporal dependencies, while STL breaks down the data into seasonal, trend, and residual parts.
Regression Analysis: This method examines the relationship between a dependent variable (like energy generation) and one or more independent variables (like wind speed and temperature). Linear regression is a simple yet effective method when the relationship is linear.
Exponential Smoothing: Techniques like Holt-Winters smoothing can identify trends and seasonality in the data by applying weighted averages.
While statistical methods are easy to implement and understand, they might struggle with capturing complex, nonlinear relationships in the data.
Machine Learning Techniques
Machine learning (ML) techniques are increasingly popular because they can handle large datasets and detect complex patterns. Some common ML methods include:
Deep Learning models: These models can capture nonlinear relationships and interactions between variables but require a lot of data and computational power. Typical deep learning architectures in the forecasting space are usually:
- LSTM – Long-Short-Term Memory Neural Networks. This architecture is a spinoff from the recurrent neural network architecture and allows the model to have both a long-term and a short-term memory component that learns during training. This allows for longer term temporal patterns to be captured by the model.
- MLP – Multilayer perceptron. This architecture is the most fundamental deep learning architecture. This can be used on its own for forecasting, but it’s usually found as a component in a larger system to transform the outputs of a more complex system.
- Transformers – Transformers are at the frontier of deep learning. This architecture is the basis of large language models like ChatGPT. This is a relatively new architecture in the forecasting space but it has shown promise in short term forecasting.
Support Vector Machines (SVMs):Effective for both classification and regression tasks, especially in high-dimensional spaces.
Random Forests and Gradient Boosting Machines: These methods improve prediction accuracy by combining multiple decision trees.
Although ML techniques can be very accurate, they come with challenges:
- Data Requirements: ML models need large amounts of high-quality data, which can be hard to obtain.
- Complexity and Interpretability: ML models, especially deep learning models, can be complex and difficult to interpret.
- Implementation: Implementing machine learning models often requires robust data engineering pipelines and monitoring platforms, which can be difficult to set up without the right expertise.
Probabilistic Forecasting
Probabilistic forecasting techniques offer users a distributional view of forecast values based on different probabilities. These forecasts are typically labelled as P10 through P90, corresponding to the 10th through 90th percentiles. The mean and median (50th percentile – P50) are usually included and serve as the standard forecast metrics. During times of certainty, the values in the distribution will be closely clustered around the median, while during times of uncertainty, they will be more widespread. These types of forecasts are crucial for managing uncertainty in renewable energy production. They are particularly important for facilities where understanding periods of certainty and uncertainty is critical, such as those providing ancillary services. By providing insight into forecast variance, these techniques help owners and operators make informed decisions about declaring availability during uncertain periods.
Nowcasting
Nowcasting is designed for very short-term renewable power forecasts, providing precision from minutes to hours ahead. It relies on real-time data from facility sensors and is most effective for intra-day forecasts.
Ensemble Methods
Ensemble models combine the strengths of various forecasting methods, typically integrating well-established mathematical models with statistical and machine learning models that capture site-specific details. These systems are effective for long-term, day-ahead, and intra-day forecasts, and are valuable for financial feasibility studies.
How do you Measure Forecast Performance?
The word “accuracy” is often bandied about in meetings. “How accurate is your model?” asks the engineer. The correct response is frequently left up for debate after the meeting concludes. A salesperson would most likely say something along the lines of, “Our model is very accurate; we’re looking at over 90%,” whilst the data scientist cringes in the background, wondering which number the salesperson has quoted and whether it truly indicates accuracy.
In reality, there’s no single, universally accepted measure for “model accuracy” that applies to all situations. The appropriate metric depends on the context, model type, and specific goals. Here are some key metrics used when referring to renewable power forecasts:
Co-efficient of Determination (R²): is most likely the metric used by the salesperson in the example above. It is notoriously the most intuitive measure and can be described as the proportion of variance in the dependent variable that is explained by the independent variables – in other words, the proportion of variability in the data explained by the model. It’s often expressed as a percentage between 0 and 100%, with higher values indicating better overall fit.
Mean Absolute Error (MAE): provides the average magnitude of error in the target variable’s units. It’s useful for indicating typical prediction precision but is less sensitive to large outliers.
Root Mean Square Error (RMSE): Similar to MAE, RMSE indicates prediction precision but is more sensitive to larger outliers due to the squaring of errors.
To summarise:
- R² is best used to describe how well the model fits the data.
- MAE is likely the metric of choice when understanding typical prediction precision or to describe precision in certain ranges.
- RMSE is great for describing the presence of large errors during forecasting.
Normalising for Interpretability
When forecasting renewable energy, it can be rather cumbersome to explain the precision or accuracy of a given model in megawatts (MW), which would be the case when using MAE or RMSE indicators. For example, a 20 MW MAE on a 60 MW wind farm represents terrible performance, whereas the same MAE on a 240 MW wind farm could be considered acceptable.
It is common practice to normalise these metrics against the nameplate capacity of the specific facility and represent the metric as a percentage of nameplate or installed capacity, a process known as “normalising”. In the basic example above, the “normalised” MAE or nMAE for the first facility is 33%, while for the second, it is 8%.
nMAE and nRMSE are far more interpretable metrics that can be used to compare model performance across facilities and forecasters.
Forecasting for the Future
As we progress deeper into the renewable energy era, the importance of accurate renewable power forecasts cannot be overstated. From maintaining grid stability and minimising financial risks to optimising energy storage and trading, precise predictions are the backbone of a robust, efficient renewable energy ecosystem. By employing a combination of physical models, statistical analysis, machine learning, and real-time data, we’re continually improving our ability to harness the power of nature reliably and effectively.
The future of energy is not just about generating clean power—it’s about predicting it with precision. As forecasting technologies continue to evolve, they will play an increasingly crucial role in accelerating the global transition to sustainable energy sources. By embracing these advanced forecasting methods, we’re not just predicting the future of energy—we’re actively shaping it for a cleaner, more sustainable world.
