AMI Network Energy Estimation via Statistical Sampling
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Solution Overview
Problem
Current Advanced Metering Infrastructure (AMI) networks face limitations in bandwidth and architecture, making it impractical to collect and analyze data from a large number of utility meters in real-time or near real-time, especially during demand response events, which is necessary for accurate load shedding and energy savings estimation.
Innovation Solution
A method and system that organizes utility participants into statistically similar groups, collects data from representative groups at predefined intervals, and uses this data to estimate energy consumption across the entire population, effectively managing network bandwidth and enabling near real-time data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from all utility meters in an AMI network, then measurement precision and reliability of demand response estimation are improved, but network bandwidth consumption increases and data collection time extends to many hours
Solution Approach 1:
The patent segments the utility customer population into multiple statistically similar groups (clusters) based on energy consumption patterns. Instead of collecting data from all customers uniformly, the system selects representative samples from each segment, allowing accurate demand response estimation while reducing data collection time and network bandwidth requirements.
Solution Approach 2:
The patent uses statistical sampling to create representative copies of the population characteristics. By selecting a statistically representative sample of customers from each segment, the system can infer population-level demand response behavior without collecting data from every customer, thus achieving measurement precision with reduced time loss.
2Measurement precision
If data is collected from all utility meters in real-time, then demand response estimation accuracy is improved, but network bandwidth and architecture limitations are exceeded
Solution Approach 1:
The patent divides the large utility customer base into manageable segments based on statistical similarity in energy consumption patterns. This segmentation allows the system to collect data from a representative sample within each segment rather than all customers, reducing the network bandwidth required while maintaining estimation accuracy.
Solution Approach 2:
The patent applies partial action by collecting data from only a statistically representative sample of customers rather than the entire population. This partial sampling approach provides sufficient accuracy for demand response estimation without overloading the network bandwidth, achieving the right balance between measurement precision and network capacity.
3Measurement precision
If granular data is collected at minute-by-minute intervals, then measurement precision of energy consumption is improved, but network bandwidth consumption and data collection time increase significantly
Solution Approach 1:
The patent segments customers into statistical groups with similar energy consumption patterns, then collects granular minute-by-minute data only from representative samples within each segment. This approach maintains high measurement precision for energy consumption while reducing overall network bandwidth utilization compared to collecting the same granular data from all customers.
Solution Approach 2:
The patent uses statistical sampling to create representative copies of customer energy consumption behavior. By collecting granular data from these representative samples and using statistical inference to extrapolate to the population, the system achieves high measurement precision with reduced network bandwidth consumption.
Data Source
AI summary
A method and system for estimating energy consumption of a utility population includes organizing the utility population comprising energy consumers into a plurality of groups. Next, a distribution of energy consumption against the plurality of groups may be calculated. Subsequently, statistically representative groups based on the energy distribution and the plurality of groups may be determined. Data is then collected from the statistically representative groups at predefined intervals from a communications network. Energy consumption of the utility population may then be estimated based on the data collected from the statistically representative groups. The communications network comprises an advanced metering infrastructure (AMI) network.