Deep Transformer Hyperparameter Tuning for Wind Power Prediction
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Solution Overview
Problem
Conventional neural networks face difficulties in accurately predicting wind power due to the instability and volatility of wind energy, which results in long model training times and inefficiencies in processing complex time sequences.
Innovation Solution
A wind power prediction method that optimizes a deep Transformer network using the whale optimization algorithm (WOA) to improve efficiency and accuracy by performing maximum and minimum normalization, initializing hyper-parameters, and updating them through various optimization mechanisms to construct a WOA-Transformer wind power prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks (RNN, LSTM, GRU) are used to process wind power time sequence data, then the model can capture temporal dependencies, but the training time becomes excessively long due to inability to perform parallel operations
Solution Approach 1:
The patent replaces the sequential processing mechanism of conventional neural networks (RNN, LSTM, GRU) with the parallel processing capability of the Transformer architecture. The self-attention mechanism in Transformer allows simultaneous computation of relationships between all time steps, eliminating the sequential bottleneck while maintaining the ability to capture temporal dependencies in wind power data
Solution Approach 2:
The patent introduces the attention mechanism dimension that allows the model to weigh and process all time steps simultaneously rather than sequentially. The self-attention layer computes relationships across the entire time sequence in parallel by calculating attention scores between all pairs of time steps, fundamentally changing the processing dimension from sequential to parallel
2Measurement precision
If manual parameter adjustments are made for neural network models, then the model can be adapted to specific datasets, but the process becomes complex and time-consuming
Solution Approach 1:
The patent implements self-service through the whale optimization algorithm that automatically searches for and determines optimal hyper-parameters without requiring manual intervention. The algorithm independently evaluates different parameter combinations based on prediction performance and converges to the optimal configuration, eliminating the need for complex manual parameter tuning while maintaining high prediction accuracy
Solution Approach 2:
The patent systematically varies and optimizes key hyper-parameters including learning rate, batch size, number of layers, and attention heads using the whale optimization algorithm. This automated parameter exploration allows the model to adapt to the specific characteristics of wind power data without requiring manual expertise in parameter selection
Data Source
AI summary
A wind power prediction method and system for optimizing a deep Transformer network by whale optimization algorithm are disclosed. The sequence data of wind power and related influence factors are taken as sample data which is divided into a training set and a test set, where the data is trained and predicted by a Transformer network model established according to values of the initialized hyper-parameters, and an average absolute error of wind power prediction is taken as a fitness value of each whale group. A local optimal position is determined according to the initial fitness value of individual whale group, and the current optimal position is updated by utilizing whale group optimization, and the best prediction effect is obtained by comparing the local optimal solution with the global optimal solution. An optimal hyper-parameter combination is obtained after multiple iterations of the whale optimization algorithm, and the wind power is predicted.