Solar power forecasting

Table of Contents

Solar power forecasting

Solar power forecasting is the process of predicting a photovoltaic (PV) system’s future electricity generation. It is also used to optimize battery capacity adjustments based on forecasts of PV production and household consumption to minimize curtailed PV power.

Why solar power forecasting matters

Solar power forecasting, otherwise known as PV forecasting, is an important tool for holistically optimizing energy systems. Integrating PV systems with battery storage enables the most efficient and intelligent use of solar energy. In advanced energy management systems (EMS), particularly those employing dynamic pricing strategies like Time-of-Use (ToU) optimization, precise PV forecasting is crucial. Accurate predictions achieved through diverse modeling methods, such as physical, statistical and integrated forecasting technologies, ensure effective planning and utilization of solar resources. This forecasting capability enhances the overall reliability and efficiency of energy systems.

Solar forecasting methods 

There are different methods and techniques to predict sunlight availability for efficient energy optimization in different scenarios. These solar power forecasting methods have evolved significantly over time. Sometimes, a combination of these approaches enables the most effective and accurate solar power forecasting. All methods can be traced back to three foundations. 

Solar forecasting methods

Physical methods

Physical solar forecasting is a predictive approach that relies on numerical weather prediction (NWP) models, sky imaging and satellite imaging to estimate solar power generation by simulating the behavior of the atmosphere, sunlight and cloud cover, allowing for more accurate forecasts of photovoltaic energy output based on the physical characteristics of the environment. 

Statistical methods

Statistical methods employ methods like persistence forecasting, which leverages historical and real-time generation data to statistically adjust predictions derived from numerical weather prediction models. Persistence forecasting, a straightforward statistical technique, assumes that current solar generation levels will persist in the very near future, thus serving as a reference model for evaluating more sophisticated forecasting methods. 

Holistic and integrated methods

This is where a forecast-based energy management system comes into play, which combines various forecasting strategies, including the two mentioned above. It integrates real-time data, such as rooftop PV production, battery status and load consumption with external information like electricity prices and weather forecasts. Such integration empowers the energy management system to make intelligent decisions on battery charging and grid interaction to continuously optimize the energy flows between all assets in clean energy systems.                               

Types of Forecasting

It is important not to confuse the forecasting types below with umbrella forecasting techniques, such as numerical weather prediction or persistence forecasting or artificial intelligence (AI)-based approaches. While these approaches provide a broad overview of weather conditions, specialized forecasting types refine predictions for the unique characteristics of solar power generation. The following are different types of forecasts employed in various forecasting methods mentioned above.

types of forecasting used in solar power forecasting

Short-term forecasting

Short-term forecasting encompasses predictions within a one- to six-hour time frame and is crucial for scheduling, load-following and congestion management. It is usually derived by blending statistical methods with numerical weather prediction models to offer precise insights into near-future solar energy generation. 


This involves predicting solar power generation in the very near future, typically within the next few minutes to hours. Nowcasting relies on real-time data and advanced monitoring technologies to provide accurate and immediate forecasts. 

Intra-day forecasting

Focused on predicting solar power generation within a single, intra-day forecasting is essential for managing grid stability and optimizing energy resources. It considers factors such as weather changes, cloud cover and solar radiation variations throughout the day. 

Day-ahead forecasting 

This type of forecasting extends to the next 24 to 48 hours, providing insights for the upcoming days. It aids in strategic planning for energy markets, helping utilities optimize resource allocation, plan for maintenance and manage energy demand effectively.

Benefits of short-term forecasting

For grid operators, short-term forecasting enables improved network management and system balancing by allowing them to anticipate fluctuations in the solar power generation and better plan for grid stability. Renewable generators, on the other hand, benefit from the ability to optimize their intra-day and day-ahead electricity market trading strategies, increasing the profitability and efficiency of their operations.

Medium-term to long-term forecasting

With a timeframe spanning several days to a year ahead, a medium to long-term solar power forecast is instrumental in applications such as scheduling, reserve requirement planning, marketing trading strategies and congestion management. It is typically generated through NWP models with corrections to account for systematic biases. This provides valuable insights into solar energy generation for mid-range planning and operational decision-making. 

Week-ahead forecasting 

This forecast extends beyond the daily horizon, providing insights for the upcoming week. It aids in strategic planning for energy markets, helping utilities optimize energy purchases and manage long-term resource planning. 

Seasonal forecasting 

Extended to cover weeks or even months, seasonal forecasting helps anticipate solar power patterns during different seasons. It plays a crucial role in planning for variations in energy demand and adjusting resource portfolios accordingly. 

Year-ahead forecasting 

This is a long-term forecast that spans an entire year. It is valuable for energy policy planning, infrastructure development and making informed decisions about capacity expansion/contraction based on expected solar power generation trends. 

Specialized forecasts 

These are tailored methodologies designed to provide precise and context-specific predictions for optimizing solar energy generation and utilization.

Spatial forecasting 

This considers the geographical distribution of solar resources, helping to optimize energy production across diverse locations. 

Uncertainty forecasting

Uncertainty forecasting recognizes and quantifies uncertainties in solar power forecasts, providing a range of possible outcomes. This is crucial for decision makers to account for potential variations in their planning. 

benefits of short-term vs long-term weather forecasting relevant to solar power forecasting

Benefits of long-term forecasting

Long-term weather prediction is valuable for system operators as it aids in reserving planning and operation management, allowing them to make informed decisions about resource allocation and grid infrastructure development. Additionally, it helps operators prepare proactively for extreme weather events, reducing the risk of power disruptions and ensuring grid resilience. 

Whereas, for renewable generators, long-term prediction facilitates efficient placement for renewable plants by providing insights into optimal locations for solar and wind facilities, maximizing energy production potential and reducing investment risks associated with suboptimal siting decisions. This foresight ensures that renewable energy projects are strategically positioned to harness the full potential of clean energy resources, contributing to long-term sustainability and profitability. 

Challenges in solar forecasting 

Solar forecasting is crucial for the efficient integration of solar energy into the grid. It enables grid operators to anticipate fluctuations in solar power generation, allowing for proactive adjustments to maintain grid stability. This anticipation is vital as solar power is contingent on weather conditions, and accurate forecasting helps in balancing the supply and demand dynamically. However, it comes with inherent limitations. 

Solar power forecasting

Intermittent nature of solar power 

The intermittent nature of solar power, due to constantly fluctuating weather patterns and daily variations, poses challenges for accurate forecasting. Cloud cover, changing sunlight levels and unexpected meteorological and climatological events make it difficult to reliably predict solar energy production. Advanced modeling and data analysis are crucial to address these uncertainties and enhance the effectiveness of solar forecasting approaches. 

Uncertainty in weather data 

Closely related to the intermittent nature of solar power, uncertainty in weather data is a major hurdle in solar forecasting. Weather conditions heavily influence solar power generation, and inaccuracies in forecasting weather parameters like cloud cover, precipitation and wind can lead to unreliable solar forecasts. Overcoming these uncertainties through improved meteorological data and advanced modeling techniques, particularly through accurate real-time data, is vital to adapt to rapid changes in solar power generation  and thereby enhance solar energy integration. 

Solar curtailment

Solar curtailment, or the excess solar power that cannot be absorbed by a household or the grid, presents a significant challenge in solar forecasting. Accurate predictions are essential to minimize curtailment and maximize solar energy utilization. Factors, such as grid constraints, demand fluctuations and variable solar generation, complicate this task, demanding sophisticated forecasting models and grid management. 

Spatial variability

Spatial variability, or the differing solar irradiance (solar power per unit area) across regions, presents challenges in solar forecasting. Solar installations are often dispersed, and local factors like shading and terrain affect irradiance. Accurate forecasting demands high-resolution data and models that can capture these variations, making spatial variability a significant concern in solar energy prediction.


Advanced forecasting models 

Advanced forecasting models offer a promising solution to the challenges of solar forecasting. These models leverage machine learning, historical and high-resolution weather information to improve accuracy. By integrating these models, we can enhance our ability to predict solar power generation, reducing curtailment and ensuring better grid integration of renewable energy sources. 

High-frequency weather data 

High-frequency weather data, such as minute-by-minute updates, is a solution to improve solar forecasting accuracy. This real-time data allows grid operators to react swiftly to changing weather conditions, enhancing the reliability of solar power predictions. Incorporating high-frequency data into forecasting models enables more effective grid management and reduced curtailment of solar energy. 

Smart meter data

These devices provide real-time information on household consumption, enabling more accurate predictions of grid demand. By integrating smart meter data with PV forecasting models, we can optimize grid operations, minimize curtailment and ensure efficient utilization of solar energy resources.

Energy Management System

Energy management system

PV forecasting plays a significant role within an energy management system (EMS), which integrates real-time data, advanced forecasting models, high-frequency weather updates and smart meter information to facilitate holistic and intelligent optimization.

Simultaneously, if an EMS uses solar power forecasting together with a time-of-use tariff, businesses and end users can strategically maximize solar power and minimize costs. Accurate predictions of solar generation allow for optimized energy consumption during peak sunlight hours, aligning with lower electricity rates and promoting sustainable energy efficiency.

Moreover, through the integration of communication protocols, such as OCCP, EEBus and SG-Ready, an EMS establishes effective connections between PV systems and other energy assets such as batteries, electric vehicles and heat pumps. This connectivity facilitates self-sufficiency optimization, empowering the home energy management system (HEMS) to maximize the utilization of locally generated solar energy for powering homes. 

These advancements in solar power integration  enable more efficient grid management, minimized curtailment and more effective utilization of solar energy resources.  

Future Outlook

Future outlook of solar power forecasting

Photovoltaic (PV) energy is poised to become a major global electricity source due to its abundance, affordability and scalability, and PV forecasting is a key element in its successful integration and utilization. It enables better grid management, facilitates the efficient use of energy storage, enhances grid stability and supports economic decision-making for both individual solar users and larger utility-scale projects.

However, the variable nature of weather poses challenges, making accurate PV energy forecasting – which reduces generation-load mismatch and supports PV integration – essential. 

Machine learning-based solar power forecasting plays a pivotal role in the continued advancement of smart energy management systems. “Leveraging machine learning-based forecasts enables real-time forecasting adjustments, leading to a more efficient grid and market integration of solar power, which in the end, is beneficial for both businesses and end consumers,” says Vjekoslav Salapić, gridX’s Product Manager for Time-of-Use Optimization.

Solar energy technology is constantly advancing, and machine learning is the next big thing. These processes excel at modeling intricate relationships among weather data, PV components and location and improving the overall forecasting precision.