Machine Learning Energy Production Forecasting for Renewable Assets
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Solution Overview
Problem
Predicting energy production from renewable sources like wind turbines and solar panels is challenging due to environmental factors that are beyond the control of operators, leading to inefficiencies and revenue optimization issues for energy generating assets.
Innovation Solution
The use of machine-learning models, specifically neural networks, to establish relationships between measurable data values such as wind speed and energy output, enabling accurate forecasting and anomaly detection by evaluating input data based on multivariate sensor data and inferred operating states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used for energy production forecasting, then the system is simpler to implement, but the prediction accuracy deteriorates due to inability to account for dynamic environmental relationships
Solution Approach 1:
The patent replaces traditional mechanical prediction methods with machine learning models that automatically learn and adapt to dynamic environmental relationships. The system uses neural networks to process multivariate sensor data from wind turbines and solar panels, substituting complex mathematical modeling with data-driven approaches that improve prediction accuracy while managing system complexity through automated learning.
Solution Approach 2:
The system changes parameters by incorporating multiple environmental variables (wind speed, direction, temperature, solar irradiance) that dynamically affect energy production. The machine learning models continuously adapt to changing parameter relationships, allowing accurate predictions despite varying environmental conditions beyond operator control.
2Measurement precision
If machine-learning models with multivariate sensor data are implemented, then prediction accuracy improves, but the computational requirements and data processing complexity increase
Solution Approach 1:
The patent segments the energy generation system into multiple independent monitoring units, each with its own machine learning model processing local sensor data. This segmentation allows distributed computation that reduces central processing complexity while maintaining high prediction accuracy for each asset. The system divides multivariate sensor data into manageable streams that can be processed independently and aggregated.
Solution Approach 2:
The machine learning models operate autonomously, self-adjusting to environmental patterns without requiring complex manual configuration or intervention. The systems self-train on incoming sensor data, automatically adapting to changing conditions while minimizing the need for human expertise in data processing and model management.
3Loss of information
If real-time multivariate data analysis is performed to infer operating states, then operational awareness improves, but the computational load and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and maintaining ready-to-use machine learning models that can rapidly infer operating states when needed. Historical data is pre-analyzed to establish baseline patterns, enabling faster real-time decision-making without requiring extensive computational analysis at the moment of prediction.
Solution Approach 2:
The patent replaces time-consuming manual analysis of operating states with automated machine learning inference. The models continuously process multivariate sensor data in the background, substituting slow human assessment with rapid automated state determination that provides real-time situational awareness without significant processing delays.
Data Source
AI summary
Predicting energy production for energy generating assets, including: receiving current and forecasted meteorological data associated with a location of an energy generating asset; and predicting an energy production value produced by the energy generating asset at a predetermined time based on the current and forecasted meteorological data using a trained model for the energy generating asset, the trained model being trained using a machine learning algorithm that utilizes historical meteorological data associated with the location of the energy generating asset and historical production capability data associated with a historical production capability of the energy generating asset.