Enhancing Wind Power Forecasting through Quality Control and Data Cleaning

Forecasting wind power output presents a significant challenge for weather forecasters, primarily due to the limited availability of accurate and high-resolution wind power data. This scarcity hampers the development of reliable and precise forecast models, which are essential for optimizing the integration of renewable energy sources into power grids. To address these challenges, meteoblue AG has developed the mLM (meteoblue Learning Multimodel), a cutting-edge forecasting system that integrates multiple advanced methodologies. These include real-time nowcasting, statistical approaches such as Kalman filtering and Model Output Statistics (MOS), and proprietary machine learning algorithms. Together, these techniques enable substantial advancements in renewable energy forecasting accuracy.

A cornerstone of the mLM system is its rigorous quality control (QC) process, which is specifically designed to address the complexities of wind power data. Accurate QC is vital for distinguishing genuine meteorological variations from non-meteorological disruptions that can introduce biases into datasets. For example, curtailments of power production caused by grid limitations, regulatory noise restrictions, or scheduled plant maintenance often distort the raw data. The mLM system incorporates a robust, generalized QC framework capable of systematically identifying and addressing these anomalies, ensuring clean and reliable datasets for model training. This process significantly enhances the system’s ability to produce dependable and accurate forecasts.

The output of such QC routines is collected by meteoblue into highly localized, customized reports tailored to each model training process. These reports enable a detailed understanding of site-specific conditions and support targeted improvements in forecasting performance. The mLM system delivers location-based forecasts for both solar and wind power plants, ensuring precision at scales relevant to operational decision-making. By focusing on site-specific data quality and integrating localized forecasting techniques, meteoblue empowers renewable energy operators to optimize power generation and grid integration effectively.

The combination of advanced forecasting techniques, robust QC processes, and site-specific customization makes the mLM system a comprehensive solution for addressing the challenges in renewable energy forecasting. This integrated approach highlights meteoblue’s commitment to delivering reliable, high-resolution forecasts that support the sustainable growth of renewable energy systems worldwide.