Water Demand Forecasting With Spatial Correlation Adjustment
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Solution Overview
Problem
Conventional water demand forecasting methods fail to accurately account for spatial correlations between adjacent regions, leading to lower objectivity and accuracy due to the assumption of isolated water consumption systems, which neglects spillover effects from socio-economic developments and shared characteristics.
Innovation Solution
A water demand forecasting method that constructs a network structure model to determine spatial water demand correlation strength, combining it with temporal memory models to adjust forecasts, ensuring both temporal continuity and spatial correlation are considered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional water demand forecasting methods (quota analysis or trend extrapolation) are used, then the forecasting process is simple, but the forecasting accuracy and objectivity deteriorate due to neglecting spatial correlations between adjacent regions
Solution Approach 1:
The patent merges temporal memory models with spatial network structure models to create a comprehensive spatiotemporal forecasting system. The temporal model captures time-dependent patterns while the network model captures spatial correlations between adjacent regions, and their combination resolves the contradiction by improving accuracy through integrated spatiotemporal analysis rather than separate treatments
Solution Approach 2:
The patent introduces spatial correlation coefficients as an intermediary element that quantifies the influence between adjacent regions. This intermediary enables the transfer and adjustment of forecasting results across regions, allowing the system to account for spatial dependencies without requiring direct complex coupling between all regional models
2Reliability
If spatial correlations between adjacent regions are incorporated, then water demand forecasting accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the forecasting system into distinct modular components: temporal memory models for individual regions, network structure models for spatial relationships, and correlation coefficient calculations. This segmentation allows each component to be developed and optimized independently while maintaining overall system reliability through their coordinated integration
3Ease of manufacture
If isolated system assumptions are made for water consumption regions, then the forecasting model is simpler to implement, but the objectivity deteriorates due to neglecting spillover effects from socio-economic developments in adjacent areas
Solution Approach 1:
The patent transitions from static isolated region assumptions to dynamic spatiotemporal interactions. The temporal memory models capture evolving patterns over time, while the network structure models capture dynamic spatial influences that change as socio-economic conditions evolve in adjacent regions, enabling the system to adapt to changing conditions rather than relying on fixed assumptions
Data Source
AI summary
A water demand forecasting method includes: preprocessing first raw water consumption data to obtain first historical water consumption data and preprocessing second raw water consumption data to obtain second historical water consumption data; constructing a network structure model based on the first historical water consumption data and the second historical water consumption data, and acquiring a spatial water demand correlation strength output by the network structure model; inputting the first historical water consumption data into a temporal memory model for forecasting a water demand of the first water consumption region based on temporal dependencies in the first historical water consumption data, resulting in a first temporal water demand forecast result; and applying spatial adjustment, based on the spatial water demand correlation strength and the second historical water consumption data, to the first temporal water demand forecast result, to obtain a target water demand result.