Residential Load Forecasting via Customer Clustering
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Solution Overview
Problem
Existing Demand Response (DR) programs face challenges in accurately and efficiently predicting energy loads for the residential sector due to the large number of small energy consumers, leading to scalability issues and low prediction accuracy.
Innovation Solution
The method involves grouping residential customers into clusters based on their energy consumption behaviors and generating specific energy consumption models for each cluster to forecast energy demand, allowing for more accurate and efficient energy load predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual residential customers are modeled separately, then prediction accuracy could be improved, but computational cost and scalability deteriorate
Solution Approach 1:
The patent segments the large population of residential customers into smaller clusters based on energy consumption behavior patterns. Instead of modeling each individual customer separately, customers are grouped into behavior-based clusters, and a single model is developed for each cluster. This segmentation approach maintains prediction accuracy by capturing individual behavior patterns through cluster-specific models while dramatically reducing computational complexity compared to individual modeling.
2Loss of information
If C&I sector forecasting process is applied to residential sector, then individual customer analysis is possible, but the process becomes inefficient due to large number of small consumers
Solution Approach 1:
The patent creates representative copies of customer behavior patterns through clustering. Instead of analyzing each individual customer, the system identifies typical behavior patterns and creates cluster representations that capture these patterns. Each cluster serves as a copy or representative model of multiple customers with similar behaviors, allowing efficient forecasting while preserving essential behavior details through the cluster-specific models.
3Productivity
If residential customers are grouped into clusters, then computational cost is reduced, but model complexity increases
Solution Approach 1:
The patent changes the modeling parameters by shifting from individual customer parameters to cluster-level parameters. Instead of developing models with numerous individual customer-specific parameters, the system develops models using cluster-level aggregated parameters that represent typical behavior patterns. This parameter transformation reduces the overall model complexity while maintaining the ability to capture essential behavior variations across different customer segments.
Data Source
AI summary
A method includes obtaining energy consumption information representative of energy consumption behaviors of multiple customers, grouping the multiple customers into multiple different clusters based on the consumption behaviors of the multiple customers, and generating an energy consumption model for each different cluster to enable forecasting of energy demand of the multiple customers.