Accurate forecasting of renewable energy production, particularly for wind and solar power, is critical due to the stochastic nature of these energy sources. Renewable energy is highly dependent on weather conditions, which can vary significantly over different timescales. As a result, precise forecasting is essential to ensure the efficient and reliable integration of renewable energy into the grid. The success of such forecasts largely depends on the accuracy of Numerical Weather Prediction (NWP) models.

Leveraging multiple NWP sources can greatly improve the accuracy of short-term renewable energy forecasts. NWPs provide estimates of future weather conditions, and analyzing weather events from multiple sources enhances prediction accuracy by offering a more detailed understanding of meteorological conditions. Also, every model uses its own data assimilation methods and different initial boundary conditions. It is also useful to detect potential weather scenarios. Additionally, using multiple NWP sources provides prediction intervals and provides better understanding about forecast uncertainty to evaluate weather events accurately. While some models work better in a region, others do not. Multiple NWP sources have some benefits under the region-specific conditions. With all those mentioned benefits, using several NWP sources obviously provides major benefits for energy forecasting modelling.

Both statistical and physical renewable energy forecasting models rely on NWP data, and the accuracy of these models is correlated with the performance of the NWPs. NWPs are typically run multiple times a day to provide timely information, and accurate forecasts are more achievable under stable weather conditions. The major benefits of using multiple NWP sources in renewable energy production forecasting include improved forecast accuracy, a broader range of weather scenarios, reduced forecasting uncertainty, and enhanced location-specific forecasting.

At Algopoly, we produce highly accurate wind and solar energy forecasts by utilizing numerous NWP sources. Our approach fully considers the geographical and weather stability factors for each renewable energy farm, ensuring reliable forecasts supported by robust data handling practices.

Data handling is a crucial component in forecasting renewable energy production, particularly for solar and wind farms. Accurate forecasts depend on the effective management of historical production data and multiple NWP datasets. This involves cleaning, preprocessing, and integration of data to build reliable forecasting models. Poor data handling can result in inaccurate forecasts, leading to significant operational and financial costs.

• Outlier Detection: Outlier detection is essential in preventing skewed results and ensuring the accuracy of forecasting models. Outliers can distort statistical measures and create misleading patterns. Detecting and managing outliers reveals true underlying data patterns, resulting in more accurate analysis. Outliers can arise from sensor errors, maintenance activities, or unusual weather events. At Algopoly, we use statistical methods to identify and address outliers, ensuring robust and reliable forecasts.
• Capacity Changes Over Time: Renewable energy production capacity is dynamic, fluctuating due to maintenance, upgrades, or natural degradation. Accounting for these changes is vital for maintaining accurate forecasting models.
• Anomalies Due to Extreme Weather Conditions: Extreme weather conditions can cause anomalies in production data, impacting the integrity of forecasting models. Incorporating mechanisms to detect and adjust for these anomalies ensures model accuracy. Analyzing historical weather data helps understand its impact on production, allowing for models that anticipate and adjust for such anomalies. By integrating data preparation methods with real-time production data and high-resolution inputs, predictive performance can be significantly enhanced, leading to more reliable renewable energy production forecasts.

Machine learning offers various models to solve forecasting problems, each with its strengths and weaknesses. Ensembling, or combining different machine learning models, leverages their diverse tendencies to improve overall performance. This method reduces overfitting and enhances reliability by averaging out individual errors. Overfitting occurs when a model learns random noise instead of generalizing the training data, leading to poor performance on unseen data. Ensembling mitigates overfitting by averaging errors, helping the model generalize better.

Reliability refers to the variance of prediction errors. In a live prediction system, stable error distribution is desirable. Ensembling improves reliability by using diverse models, reducing the impact of individual errors. At Algopoly, we build various machine learning models to obtain diverse predictions, then ensemble the best-performing models to achieve the final forecast. This approach allows continuous improvement by integrating new models without starting from scratch, enhancing the overall reliability and accuracy of our forecasting system.

Our methodology leverages advanced model development stages and incorporates real-time operational data to enhance forecast precision. This approach emphasizes the integration of high-resolution data and real-time feedback to mitigate the impact of weather discrepancies and planned outages on forecast performance.

Continuous integration of intra-day actual production values allows our models to adjust dynamically, improving the accuracy of short-term predictions.

Integrating real-time data is crucial for enhancing accuracy and reliability. Real-time production values enable dynamic adjustments based on the latest operational data, ensuring that any deviations from expected performance are promptly accounted for. This significantly improves the precision of short-term forecasts, critical for operational planning and decision-making. Continuous data feeds reduce latency between actual production and forecast adjustments, enabling responsive and accurate predictions.

The resolution of input data is a critical factor influencing forecast accuracy. We integrate real-time plant data at the highest possible resolution, typically with updates every 15 minutes, to capture rapid changes essential for accurate wind energy predictions. Detailed operational data from renewable energy plants allow the model to understand performance variations at a granular level, leading to more precise forecasts. High-resolution data utilization reduces uncertainty, enhances forecast accuracy, and allows for fine-grained analysis of weather patterns and operational parameters.

Collaboration with renewable energy plant operators allows us to integrate planned maintenance schedules into our forecasting models, enabling us to anticipate and compensate for production dips.

By incorporating real-time production data and utilizing high-resolution inputs, we enhance forecast accuracy and support reliable renewable energy integration into the power grid. This approach underscores the importance of data communication and continuous model refinement in achieving superior forecast performance.