Interpretable Power Load Prediction Using Deep Exponential Smoothing
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Solution Overview
Problem
Existing power load prediction methods face a trade-off between performance and interpretability, with statistical models being less accurate but more interpretable, while neural networks offer better performance but lack clarity in their decision-making processes.
Innovation Solution
The Deep Exponential Smoothing (DeepES) method combines exponential smoothing models with deep learning to extract time-sequence characteristics, initializing and iteratively calculating seasonal, trend, and smoothing factors to produce an interpretable power load prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network models are used for power load prediction, then prediction performance is improved, but model interpretability deteriorates
Solution Approach 1:
The patent merges exponential smoothing models with deep learning neural networks to create a hybrid prediction system. The exponential smoothing component provides interpretable trends and seasonal patterns, while the neural network captures complex non-linear relationships. This combination allows the system to achieve high prediction accuracy through the neural network's non-linear mapping capabilities while maintaining interpretability through the explicit mathematical formulas and clear logic of the exponential smoothing model.
2Loss of information
If statistical models are used for power load prediction, then model interpretability is improved, but prediction performance deteriorates
Solution Approach 1:
The patent combines the strengths of statistical exponential smoothing models with modern deep learning techniques. The exponential smoothing part maintains interpretability through its clear mathematical structure and explicit trend/seasonal decomposition, while the integrated neural network component enhances prediction performance by learning complex patterns from historical data that traditional statistical methods miss.
Data Source
AI summary
The present invention provides an interpretable power load prediction method, system and terminal machine, relating to the field of power load prediction. The method comprises: initializing three factors—seasonal factor, trend factor, and smoothing factor, denoted as S1, T1, and I1 respectively; calculating states of the three factors for time t+1 in a current DeepES unit; outputting the three factors St+1, Tt+1, and It+1 to a next DeepES unit; repeating until a n-th DeepES unit completes its operation; calculating a predicted value Y based on the three factors that are outputted from a final DeepES unit. In power load prediction, constructing an interpretable prediction model enables users to understand the inference process of the model, therefore helps enhance the credibility of the model.