Utility distribution network analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing data from battery-operated utility meters in district heating networks are limited by the restricted data capacity and non-coincident sampling, leading to inaccurate results and constraints in optimizing the distribution network.
Innovation Solution
A method that calculates flow rate and temperature using integrated flow-temperature products and accumulated volume from smart utility meters, connected to a Head End System via Advanced Metering Infrastructure, allowing for accurate estimates even with longer sampling intervals and reduced data concurrency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection frequency from battery-operated meters is increased to improve analytics accuracy, then measurement precision improves, but energy consumption and battery lifetime are reduced
Solution Approach 1:
The patent changes the parameter of data collection frequency dynamically. Meters collect and transmit data at low frequency during normal operation to conserve battery energy, but automatically increase collection frequency when anomalies are detected or when data quality metrics indicate the need for higher precision analytics, thus resolving the contradiction between measurement precision and energy consumption
Solution Approach 2:
The system implements dynamic data collection where the sampling frequency is not fixed but adapts based on network conditions, battery status, and analytics requirements. This allows the system to optimize between energy conservation and data quality by adjusting parameters in real-time rather than using static high-frequency collection
2Measurement precision
If multiple meters transmit data simultaneously at high frequency to improve network monitoring accuracy, then measurement precision improves, but communication bandwidth consumption increases
Solution Approach 1:
The patent implements periodic data transmission where meters send data at scheduled intervals rather than continuously. The period between transmissions is optimized to provide sufficient monitoring accuracy while minimizing total communication bandwidth consumption. Anomaly detection triggers can shorten the period locally without requiring all meters to increase frequency simultaneously
Solution Approach 2:
Instead of all meters transmitting at maximum frequency simultaneously, the system uses partial action by having only the subset of meters most relevant to current monitoring needs transmit at high frequency, while others maintain lower transmission rates, thus achieving adequate monitoring accuracy with reduced total bandwidth consumption
3Device complexity
If static analysis methods are used to simplify network modeling, then device complexity is reduced, but reliability of detecting network changes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the analytics system continuously monitors data quality and model performance. When static analysis fails to detect known anomalies or when data patterns indicate dynamic conditions, the system automatically triggers more sophisticated dynamic analysis methods, thus maintaining reliability without permanently increasing system complexity
Solution Approach 2:
The analysis system is segmented into multiple levels: simple static analysis for normal conditions, intermediate analysis for specific scenarios, and complex dynamic analysis for anomaly detection. Each segment handles appropriate cases, allowing the system to maintain low complexity for routine operations while having the capability to achieve high reliability when needed
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for determining a flow rate and a temperature of a fluid at a selected position in a district heating or cooling utility distribution network, comprising a plurality of interconnected distribution lines and smart utility meters. The meters are arranged to register the energy delivered at consumer premisses situated along the distribution lines, the method comprising the steps of: collecting meter data time series from the smart utility meters using an Advanced Metering Infrastructure, and calculating the temperature and the flow rate at the selected position. The calculations are based on flow and temperature information derived from the collected meter data time series, the topology of the utility distribution network and heat transfer coefficients of the distribution lines. The meter data time series comprises the accumulated volume of fluid delivered to the consumer, and the integrated flow-temperature product calculated by the meter.