Attention Neural Network for Load Forecasting via Knowledge Transfer
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Solution Overview
Problem
Existing load forecasting methods face challenges in short-term load forecasting for communication traffic, particularly in domains with limited training data, where accurate predictions are crucial for efficient resource allocation and system performance.
Innovation Solution
The method involves obtaining source data sets and a target data set from multiple source and target base stations, selecting machine learning models with high traffic load prediction performance through negative transfer analysis, and combining these models using an attention neural network to create a load forecasting model for the target base station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transfer learning is applied to improve learning performance in the target domain by utilizing knowledge from source domains, then prediction accuracy is improved, but model complexity increases due to combining multiple source models and target models
Solution Approach 1:
An attention mechanism is introduced as an intermediary component to selectively combine predictions from multiple source models and the target model. The attention mechanism computes weights based on the similarity between source and target domains, allowing the system to adaptively select which models to trust without manually specifying combinations. This resolves the complexity issue by providing an automated, data-driven model selection process.
Solution Approach 2:
The system dynamically adjusts the parameters (weights) of the combined models based on the measured similarity between source and target domains. When domains are highly similar, more source models are weighted heavily; when similarity is low, the target model is weighted more. This parameter adaptation allows the system to maintain high accuracy while managing complexity through conditional model usage.
2Reliability
If multiple source models are combined with the target model to improve forecasting accuracy, then prediction performance is improved, but computational resources and processing time increase
Solution Approach 1:
Instead of always using all available source models, the system applies partial action by selectively using only the top-k most similar source models based on domain similarity metrics. The attention mechanism naturally performs this selection by assigning higher weights to more similar domains and lower weights to less similar ones, reducing computational burden while maintaining accuracy.
Solution Approach 2:
The model combination strategy is dynamic rather than static. The system adjusts which models are actively used based on real-time similarity assessments between source and target domains. This dynamic adaptation allows the system to optimize computational resources by activating only the necessary models for each forecasting task, rather than maintaining all models in memory and execution.
3Adaptability or versatility
If domain similarity is used to select source models for transfer learning, then appropriate knowledge transfer is achieved, but the system complexity increases due to similarity measurement and model selection processes
Solution Approach 1:
The attention mechanism serves multiple functions simultaneously: it measures domain similarity, selects appropriate source models, computes combination weights, and generates final predictions. This multi-functionality consolidates what would otherwise require separate complex subsystems into a single integrated component, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The system performs self-service by automatically measuring domain similarity and selecting source models without external intervention. The attention mechanism inherently performs the model selection process based on data-driven similarity metrics, eliminating the need for manual feature engineering or complex pre-processing steps to identify appropriate source domains.
Data Source
AI summary
A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.