Wind Power Forecasting via Segmented Model Ensemble
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Solution Overview
Problem
Wind power generation systems face challenges in accurately forecasting electrical power production due to varying wind conditions, leading to inefficiencies in power grid management and energy trading.
Innovation Solution
A system that utilizes sensors to collect and process weather and wind turbine data, selects and trains models to predict electrical power production, and uses a feedback loop to continuously improve forecasting accuracy, incorporating data filtering and model reconfiguration techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used for wind power generation, then the system operation is simpler, but the forecasting accuracy is insufficient leading to inefficiencies in power grid management
Solution Approach 1:
The forecasting system is segmented into multiple specialized models including persistence models, numerical weather prediction models, and reanalysis models. Each model handles specific aspects of wind power forecasting, allowing the system to achieve high accuracy through divided functionality rather than a single complex model
Solution Approach 2:
A data processing intermediary layer is introduced between raw weather data and forecasting models. This intermediary performs data quality assessment, filtering, and preprocessing, enabling accurate forecasts without requiring direct complex processing of all raw data
2Measurement precision
If multiple forecasting models are used to improve accuracy, then the forecasting precision increases, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system applies partial action by selectively using different models based on forecast time horizons. Persistence models are used for short-term forecasts where they are sufficient, while more computationally intensive models are applied only when needed for longer-term predictions, avoiding unnecessary processing time
Solution Approach 2:
Data preprocessing and quality assessment are performed in advance before feeding data to forecasting models. Historical data is pre-processed and stored in optimized formats, reducing the computational burden during actual forecasting operations and minimizing data processing time
3Measurement precision
If real-time data processing is implemented to improve forecast accuracy, then the prediction precision improves, but the system requires more computational resources and processing power
Solution Approach 1:
The system maintains continuous forecasting operations using a ensemble of models that run concurrently rather than sequentially. Multiple models process data in parallel, distributing computational load and energy consumption across multiple simpler processes rather than requiring intense computational bursts in single processes
Solution Approach 2:
The system uses persistence models that copy and extrapolate recent patterns without requiring intensive reprocessing of all historical data. These simplified copy-based models handle routine forecasting needs with minimal computational energy, reserving intensive processing only for model updates and anomaly detection
Data Source
AI summary
A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.