Demand Forecasting With Dynamic Realignment in Distribution Networks
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Solution Overview
Problem
Current simulation forecasting systems for large scale distribution networks fail to accurately predict volume changes due to dynamic realignments of nodes and edges, as they rely on historical data and proportion adjustments, and are unable to handle multiple realignments within a forecast period, requiring computationally expensive retraining.
Innovation Solution
A system that includes a processor configured to generate estimated or actual volume features based on demand volume shifts, using a trained forecasting model to create a demand forecast data structure, which accounts for non-historical volume shifts and realignments in large scale distribution networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data and proportion adjustment are used for forecasting, then the system is simple to operate, but it fails to capture unexpected volume shifts from dynamic realignments
Solution Approach 1:
The system performs preliminary detection of realignments before generating forecasts. By identifying realignment events in advance and comparing them against threshold values, the system prepares adjusted input data that accounts for expected volume shifts, allowing the forecasting model to produce accurate predictions without requiring complex real-time adjustments during the forecasting process itself
Solution Approach 2:
The system changes the parameter representation by introducing estimated volume features and actual volume features as distinct input types to the forecasting model. When realignments are detected, the system transforms the input data by generating estimated volume features based on historical patterns, whereas without realignments, it uses actual historical volume data. This parameter transformation allows the model to adapt to different scenarios without structural changes
2Measurement precision
If the system handles multiple realignments within a forecast period, then forecasting accuracy improves, but computational cost increases due to required retraining
Solution Approach 1:
The system implements a dynamic approach where the forecasting process adapts to the number and nature of realignments occurring within a forecast period. The system dynamically determines whether to generate estimated volume features or use actual historical data based on realignment detection, allowing it to handle multiple realignments without requiring full model retraining. This dynamic adaptation maintains accuracy while controlling computational costs
Solution Approach 2:
The system creates estimated volume features as synthetic copies of historical data patterns when realignments are detected. Instead of retraining the model on new data, the system generates estimated features that replicate the expected impact of realignments based on historical patterns, allowing the existing trained model to accurately predict outcomes of multiple realignments without additional training computational cost
3Adaptability or versatility
If proportion adjustment is applied to realignments, then reassignment of volume within forecast is achieved, but it fails to consider volume differences across different nodes
Solution Approach 1:
The system applies local quality by treating each node's volume shift independently rather than using uniform proportion adjustments. The realignment detection mechanism evaluates volume changes at each specific node against threshold values, and the estimated volume features are generated node-specifically based on historical patterns for that particular node. This localized approach preserves the adaptability to handle realignments while improving prediction accuracy by considering node-specific characteristics
Data Source
AI summary
Systems and methods for simulation forecasting in target networks including dynamic realignment are disclosed. A set of target nodes including at least one demand node realignment representative of a demand volume shift for at least one corresponding distribution node in a predetermined time period is received. When the demand volume shift is equal to or above a predetermined threshold, an estimated volume feature for the at least one corresponding distribution node is generated. When the demand volume shift is below the predetermined threshold, an actual volume feature for the at least one corresponding distribution node is generated. The generated one of the estimated volume feature or the actual volume feature is provided to a trained forecasting model to generate a demand forecast data structure based on the generated one of the estimated volume feature or the actual volume feature.