Table of Contents
Introduction
Forecasting renewable generation is the practice of predicting how much electricity solar panels, wind turbines, and other variable renewable sources will produce in the future. Because many renewables depend directly on the weather, their output changes from minute to minute and from season to season. Reliable forecasts allow grid operators, power plant owners, markets, and even households to plan ahead, keep the system stable, and reduce costs.
This chapter focuses on what is specific to forecasting, especially for variable renewables such as wind and solar, and how digital tools improve it.
Why Forecasting Matters for Variable Renewables
Variable renewables, particularly wind and solar photovoltaic systems, cannot simply be turned up or down like a conventional power plant. Their production is driven by wind speed, solar radiation, cloud cover, temperature, and other weather factors. If their output is not anticipated, the grid can face imbalances between supply and demand.
Forecasting helps to reduce such imbalances. When grid operators know in advance that solar output will be low tomorrow due to clouds, they can schedule other plants, storage, or demand response measures. When a strong wind event is predicted, they can prepare transmission capacity and possibly curtail some production if needed. For plant owners, forecasts inform trading in electricity markets and maintenance planning.
In short, forecasting turns uncertain weather dependent production into a more predictable resource, which is essential when many renewables are connected to the grid.
Time Horizons of Renewable Forecasts
Forecasts are produced for different time horizons, each used for specific decisions. Very short term forecasts, sometimes called nowcasts, cover seconds to a few hours ahead. They use high frequency data from sensors, cameras, and nearby weather stations. These forecasts are important for real time grid balancing and for automatic control of power plants.
Short term forecasts cover several hours to a couple of days ahead. They rely heavily on numerical weather prediction models that simulate the atmosphere. This horizon is central for day ahead electricity markets and for scheduling the operation of conventional plants and storage.
Medium term forecasts extend from a few days to a couple of weeks. They are less precise but help with fuel purchasing, maintenance planning, and risk management. Longer term forecasts, from weeks to seasons, are more about statistical expectations than precise hour by hour values. They support strategic decisions, such as planning for periods with typically low wind or solar resources.
Although the methods overlap, each time horizon emphasizes different data sources and modeling techniques, and they serve distinct roles in system and market operation.
Core Inputs: Weather, Site, and System Data
Accurate renewable forecasts rely on combining three main categories of information. The first is weather information, mainly produced by numerical weather prediction models. These models simulate variables such as wind speed at different heights, solar radiation reaching the ground, temperature, air pressure, humidity, and cloud cover. Weather data may come from global models, regional models with finer resolution, or local measurements that refine model outputs.
The second category is site characteristics. For wind, this includes terrain roughness, obstacles, and whether the site is onshore or offshore. For solar, it includes latitude, typical cloud patterns, altitude, and local shading from buildings, trees, or mountains. The way wind speed changes with height and the way sunlight hits tilted surfaces are both strongly influenced by these site properties.
The third category is system data. This covers the technical characteristics of the installed equipment, such as the power curve of a wind turbine, the module type and inverter configuration of a solar plant, or tracking systems that change panel orientation during the day. Historical production data from the plant is especially valuable, because it reveals how the actual system responds to given weather conditions.
Digitalization brings these inputs together, often in real time, through sensors, data platforms, and communication networks. Forecast models then transform these inputs into expected power output.
Physical Models in Renewable Forecasting
Physical models use known relationships between weather conditions and power generation. For wind power, a common step is to convert wind speed at a reference height to the hub height of the turbine, then apply a turbine power curve. The power curve specifies expected power output as a function of wind speed.
An idealized relationship between wind speed and wind power for a turbine with rotor area $A$ and air density $\rho$ can be written as
$$P = \frac{1}{2} \rho A C_p v^3,$$
where $C_p$ is the power coefficient and $v$ is the wind speed. Real turbines have a maximum $C_p$ and a limited operating range. The manufacturer power curve captures these practical constraints.
For solar photovoltaic systems, physical models often start by estimating the solar irradiance on a horizontal surface from the position of the sun and atmospheric conditions, then convert this to the irradiance on the actual panel surface using geometric relationships. Module and inverter models then convert irradiance and temperature to electrical power, taking into account losses, temperature sensitivity, and efficiency.
Physical models are transparent and grounded in physics. However, they can be limited if important effects are missing or if the input weather data is not accurate enough. They are often combined with statistical methods to better match actual plant behavior.
Key physical relationship for ideal wind power:
$$P = \frac{1}{2} \rho A C_p v^3,$$
where $P$ is power, $\rho$ is air density, $A$ is rotor swept area, $C_p$ is power coefficient, and $v$ is wind speed.
Statistical and Machine Learning Approaches
Statistical and machine learning models use historical data to learn the relationship between inputs, such as forecast weather and time of day, and outputs, such as measured power. Rather than deriving equations from physical principles, they adjust internal parameters to minimize prediction error.
Simple statistical methods include linear regression or time series models that relate current production to previous production and to a few key variables like predicted wind speed or irradiance. More advanced methods include neural networks, gradient boosting, or ensemble techniques that combine multiple models.
These approaches can capture complex, nonlinear behaviors that are difficult to model physically. For example, they can implicitly learn the effect of shadowing patterns in a solar plant or turbulence in a wind farm. They can also correct systematic biases in weather forecasts.
Machine learning models rely strongly on the quality and quantity of past data. If a plant has only recently been built, or if sensors are unreliable, purely data driven methods may struggle. For this reason, hybrid approaches that combine physical understanding with data driven corrections are increasingly common.
Forecasting Wind Power
Wind power forecasting focuses on predicting the power output of individual turbines, entire wind farms, or whole regions. The process typically begins with numerical weather prediction of wind speed and direction at different heights. These forecasts are then adjusted using local measurements, terrain models, and often statistical corrections.
The adjusted wind speed at hub height is fed into turbine specific power curves. For multiple turbines in a wind farm, layout effects are considered. Turbines located behind others may experience lower wind speeds due to wake effects, which reduce power output compared to an isolated turbine. Some models explicitly represent these wakes using physical equations, while others let machine learning systems infer wake impacts from data.
Very short term wind forecasts may use only recent measurements from turbines and nearby meteorological masts, extrapolating trends over the next minutes or hours. For example, if a weather front has just passed one part of a wind farm, its movement across the rest of the farm can be inferred. This kind of intraday forecasting improves dispatch decisions and helps avoid sudden imbalances.
Uncertainty is especially significant in wind forecasting because small errors in wind speed can lead to large errors in power, due to the approximate cubic relationship between wind speed and power in much of the operating range.
Forecasting Solar PV Generation
Solar generation forecasting combines knowledge of the sun’s apparent motion with information about clouds and atmospheric conditions. Clear sky models can calculate the theoretical solar irradiance at any time and location if no clouds were present. The main challenge is to adjust this theoretical value to account for clouds, aerosols, and other real atmospheric effects.
For day ahead solar forecasts, numerical weather prediction provides estimates of cloud cover, solar irradiance at the surface, and temperature. These values are converted into expected power using information about panel orientation, tilt, tracking behavior, and system efficiency, often supported by historical production data.
For very short term forecasts, sky cameras, satellite images, and even upstream solar plants can help detect and track clouds. By observing how a cloud shadow moves across one part of a region, models can project when it will affect another solar plant. This nowcasting can significantly improve grid operators’ ability to manage fast changes in solar output.
Solar forecasting also needs to consider predictable seasonal and daily patterns, such as the low winter sun in high latitudes or monsoon seasons with frequent cloud cover. These patterns are relatively easier to anticipate than the exact timing and shape of individual cloud events.
Quantifying Forecast Errors and Uncertainty
No forecast is perfect, so it is important to quantify how forecasts deviate from actual production and to communicate uncertainty. Common error metrics include the mean absolute error, which is the average of absolute differences between forecast and actual values, and the root mean square error, which penalizes larger errors more strongly.
Forecast providers may produce probabilistic forecasts rather than single best guesses. Instead of predicting one number, they might provide a range with probabilities, for example a 90 percent chance that solar output at a certain time will lie between two values. This helps grid operators plan for both typical scenarios and less likely, but more extreme, deviations.
Ensemble forecasting is a technique that generates several forecasts with slightly different initial conditions, models, or assumptions. By comparing these, the spread of outcomes provides an indication of uncertainty. Digitalization has made ensemble approaches more feasible, since they require substantial computational power and data handling capabilities.
Understanding and reducing forecast errors is an ongoing process. Models are regularly updated as more data becomes available and as plant configurations or surrounding land use change.
Integration with Grid Operations and Markets
Forecasts of renewable generation are not produced in isolation. They feed directly into grid operation tools and electricity markets. Grid operators use them to schedule generation units, plan reserve requirements, and coordinate cross border electricity flows. If expected renewable generation is high, less conventional generation may be scheduled, and more flexibility from storage or demand response may be needed to absorb it.
In electricity markets, generators submit offers based on expected production. For example, a wind farm owner might bid a certain volume of power into the day ahead market according to the wind forecast. If actual generation later differs from this volume, imbalance charges may apply. Better forecasts therefore have direct financial consequences.
Digital platforms increasingly automate the exchange of forecast data between forecasting service providers, plant operators, market platforms, and system operators. Application programming interfaces and standardized data formats allow forecasts to be updated frequently and used in real time operations.
Role of Digitalization and Advanced Analytics
Digitalization is central to modern renewable forecasting. Sensors on turbines and solar plants provide continuous measurements of power, voltage, temperature, and other variables. Weather stations, satellites, and radar systems supply rich environmental data. Communication networks carry all this information to data centers or cloud platforms where forecasting models run.
Advanced analytics and artificial intelligence techniques can process large volumes of heterogeneous data. For instance, convolutional neural networks can analyze satellite images to detect and track cloud patterns, improving solar nowcasts. Recurrent neural networks and other time series models can learn temporal patterns in wind and solar output.
Digital twins of power plants and grids, which are virtual replicas updated with real data, can simulate how forecast weather conditions will affect performance. Forecast models can be continuously retrained as new data arrives, improving accuracy over time.
Cybersecurity also becomes important, because false or manipulated forecast data could disrupt grid operations. Secure data infrastructures and robust validation checks are built into forecasting systems to reduce such risks.
Future Directions in Renewable Forecasting
As renewable penetration grows, the demands placed on forecasting will increase. More accurate, frequent, and localized forecasts will be required, especially for regions with a high share of wind and solar. Hybrid approaches that combine physical modeling, machine learning, and domain expertise are expected to become standard.
Forecasting of combined portfolios, such as wind plus solar plus storage, will receive more attention. Aggregated forecasts at regional or national level will be refined to support system wide planning. At the same time, personalized forecasts for individual buildings, electric vehicle fleets, and microgrids will help end users manage their own energy use and participate in flexibility markets.
There is also growing interest in forecasting extreme events, such as long periods of low wind or extended cloudy weeks, which can stress energy systems dominated by renewables. Seasonal outlooks and climate informed statistics may help prepare for these situations.
Overall, forecasting renewable generation is a rapidly evolving field that links meteorology, data science, engineering, and grid operation. Digital technologies are central to these developments and will continue to improve the predictability and value of renewable energy.