Wind Power Forecast Weighting With DDPG for Changing Conditions
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Solution Overview
Problem
Existing wind power prediction methods face limitations in accurately adjusting weights of sub-models in response to changing external prediction environments, leading to reduced prediction precision due to fixed weights and inability to effectively consider local behaviors and external environmental fluctuations.
Innovation Solution
A wind power prediction method and system utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm, which allows for dynamic weight assignment of sub-models based on interaction with the prediction environment, enabling continuous learning and optimal weight adjustment to maximize rewards and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed weights are assigned to sub-models in a combined prediction model, then the overall performance of various sub-models is considered, but the prediction precision is reduced due to inability to adapt to changing external prediction environments
Solution Approach 1:
The patent applies the Dynamics principle by transforming the static fixed weight assignment into a dynamic adaptive weight assignment mechanism. The DDPG algorithm enables the combined prediction model to automatically adjust the weights of individual sub-models in real-time based on changing external prediction environments, such as wind speed fluctuations and weather conditions. This dynamic adjustment allows the system to maintain optimal prediction precision across different operational scenarios while adapting to environmental changes.
2Measurement precision
If existing variable weight combination methods are used to evaluate prediction performance based on timing change rules, then weight adjustment is implemented, but the ability to effectively extract information about external prediction environment is insufficient
Solution Approach 1:
The patent implements the Feedback principle through the DDPG reinforcement learning framework, where the system continuously monitors external prediction environment information (wind speed, weather conditions, prediction errors) and uses this feedback to adjust sub-model weights. The algorithm receives feedback in the form of reward signals based on prediction accuracy and uses this information to learn optimal weight assignments. This feedback mechanism enables the system to effectively extract and utilize environmental information for improving prediction precision.
Solution Approach 2:
The patent replaces traditional mechanical weight adjustment methods (based on simple timing rules or manual optimization) with an intelligent reinforcement learning system. The DDPG algorithm substitutes conventional optimization approaches by learning weight assignments through interaction with the prediction environment, enabling more sophisticated extraction and utilization of environmental information patterns.
3Measurement precision
If a single prediction model is used, then the system complexity is low, but optimal prediction performance cannot be achieved in all scenarios
Solution Approach 1:
The patent applies the Merging principle by combining multiple diverse prediction sub-models (such as persistence models, autoregressive models, and machine learning models) into a unified combined prediction system. Each sub-model captures different aspects of wind power prediction, and their outputs are integrated through dynamically adjusted weights. This merging approach enables the system to achieve optimal prediction performance across various scenarios by leveraging the complementary strengths of individual sub-models.
Solution Approach 2:
The patent implements Universality by designing a multi-functional combined prediction system that can handle diverse prediction scenarios using a single integrated framework. The DDPG-based weight adjustment mechanism enables the system to adaptively configure itself for different operational conditions, making the combined model universally applicable across various wind power prediction scenarios rather than requiring scenario-specific models.
Data Source
AI summary
A wind power prediction method and system based on a deep deterministic policy gradient (DDPG) algorithm is provided and relates to the technical field of wind power prediction. The method uses multiple different prediction methods to build a combined prediction sub-model, and then uses a DDPG algorithm to maximize discount benefit by using an agent in the algorithm to interact with an external prediction environment for constant trial-and-error attempts. Finally, the agent has a capability of perceiving the external prediction environment, and a capability of reasonably and dynamically assigning weights to various prediction sub-models in a combined model, so as to achieve an accurate prediction.