Demand Response Energy Reduction Prediction System
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Solution Overview
Problem
Current demand response systems lack the ability to accurately predict energy consumption reductions by customers, leading to potential revenue losses and customer dissatisfaction due to over or underutilization of demand response events.
Innovation Solution
A computing device and method that receive customer data including participation history and historical consumption values to select customers and estimate future energy consumption reductions, determining the accuracy of these estimates to optimize demand response event scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If utilities transmit demand response signals to all customers, then the coverage of demand response events is maximized, but the utility loses revenue due to overutilization of events and customers receive unnecessary signals
Solution Approach 1:
The system performs preliminary actions by estimating future energy consumption reductions before scheduling demand response events. This allows utilities to predict which customers will actually reduce consumption and schedule events only for those customers, avoiding unnecessary signal transmission and revenue loss while maintaining effective coverage.
Solution Approach 2:
The system uses feedback from historical consumption data and participation history to continuously improve estimation accuracy. By analyzing past customer responses to demand response events, the system refines its predictions of future energy reduction, enabling more precise targeting of demand response signals to customers who will actually participate.
2Loss of energy
If utilities do not schedule enough demand response events, then revenue is preserved, but energy consumption reduction goals are not met
Solution Approach 1:
The system replaces manual trial-and-error scheduling with an automated estimation system that uses historical data and algorithms to predict energy consumption reductions. This substitution enables precise calculation of the optimal number of demand response events needed to meet reduction goals while avoiding revenue loss from over-scheduling.
3Ease of operation
If demand response event scheduling is based on inaccurate estimates, then customer satisfaction decreases due to over or underutilization, but the system complexity increases
Solution Approach 1:
The system enables self-service by automatically estimating energy consumption reductions and identifying suitable customers for demand response events without requiring complex manual analysis. The automated estimation process handles data collection, analysis, and customer identification, simplifying operations while improving accuracy and customer satisfaction.
Data Source
AI summary
A computing device for use with a demand response system is provided. The computing device includes a communication interface for receiving customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event. A processor is coupled to the communication interface and is programmed to select at least one customer from the plurality of customers by considering the participation history and the historical consumption values for each of the customers. The processor is also programmed to estimate a future reduction in energy consumption for the customer based on the customer data and to determine whether the estimated future reduction in energy consumption is substantially accurate.