Regional Gas Consumption Prediction Using Spatial Address Matching
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Solution Overview
Problem
Existing methods for predicting regional natural gas consumption are inefficient due to high computational requirements and poor real-time performance, making it difficult to accurately forecast total gas consumption for regions with tens of thousands of users.
Innovation Solution
A method and system that utilize spatial address matching to identify gas users within a target region, categorize them based on gas consumption patterns, and employ a Seq2Seq LSTM neural network model for prediction, reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning approach is used to predict gas consumption for each user separately and then aggregate, then prediction accuracy can be maintained, but computational resources and computing time will far exceed acceptable load
Solution Approach 1:
The patent segments the large-scale prediction problem into two parts: (1) user-level prediction using machine learning models for individual users, and (2) regional aggregation by spatially matching predicted user consumptions to geographic regions. This segmentation allows accurate individual predictions to be made separately, then efficiently aggregated to regional totals without requiring all users to be processed simultaneously, thus reducing peak computational load while maintaining prediction accuracy.
2Measurement precision
If traditional machine learning approach is used to predict gas consumption for each user separately and then aggregate, then prediction accuracy can be maintained, but real-time performance will be poor and difficult to meet scheduling needs
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on historical gas consumption data before the actual prediction task. The models are trained offline to learn user-specific consumption patterns, then during real-time prediction, only inference is performed on new data. This preliminary training phase separates the computationally intensive model development from the time-critical prediction phase, enabling fast real-time predictions while maintaining high accuracy through pre-learned patterns.
3Use of energy by moving object
If conventional estimation based on experience and historical data is used, then computational resources are saved, but prediction accuracy becomes highly subjective and difficult to control
Solution Approach 1:
The patent replaces the mechanical/manual estimation process with an automated machine learning system. Instead of relying on subjective human experience and manual analysis of historical data, the system uses trained neural network models that automatically learn from historical consumption patterns and generate objective, data-driven predictions. This substitution eliminates subjectivity while maintaining reasonable computational resource usage through efficient model inference.
Data Source
AI summary
A method and system for predicting regional gas consumption, and a device and a cloud platform. The future gas consumption of all gas users in an entire region is predicted according to a user-specified region and date; all networked gas users in the region are extracted during a prediction process by using spatial address matching technology; and then, by means of classifying the users and sampling the users in combination with statistics, representative sample users are extracted from all the users for respective prediction, thereby reducing the computation amount and computing resource consumption, which are required for model prediction. The method and system is mounted on a cloud platform, and the cloud platform receives, by means of Internet of Things technology, gas flow data that is uploaded by gas flow metering devices of all networked gas users, and also provides an interface for interacting with the users for the outside.