Peak Demand Identification Using Historical Consumption Thresholds
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Solution Overview
Problem
Utilities face challenges in predicting peak demand events for consumable resources, as existing methods often rely on resource production capacity, which is not informative when demand is below capacity, limiting the effectiveness of demand response applications.
Innovation Solution
A computer-implemented method that calculates a peak consumption threshold based on historic consumption data, using regression models to relate resource consumption with weather data, allowing for the identification of peak demand levels without regard to production capacity, enabling utilities to initiate demand response events and optimize energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If peak demand is defined based on production capacity, then utilities can identify when demand approaches capacity limits, but this becomes uninformative when demand is below capacity and limits demand response effectiveness
Solution Approach 1:
The patent changes the reference parameter from production capacity to historical consumption patterns. Instead of defining peak demand relative to capacity limits, the system establishes thresholds based on statistical analysis of historical consumption data, allowing peak identification to work effectively whether demand is near or below capacity.
Solution Approach 2:
The patent introduces historical consumption data as an intermediary between current demand and peak identification. By comparing current consumption against historically-derived thresholds rather than capacity limits, the system enables demand response applications across a broader range of operating conditions.
2Quantity of substance
If utilities rely on production capacity data for peak demand forecasting, then they can ensure adequate resource availability, but they cannot effectively initiate demand response programs when demand is below capacity
Solution Approach 1:
The system shifts from using production capacity as the reference parameter to using historical consumption thresholds. This enables demand response programs to be triggered based on consumption patterns rather than capacity utilization, making them effective even when demand is below available capacity.
Solution Approach 2:
The patent performs preliminary analysis of historical consumption data to establish peak demand thresholds before demand response programs are needed. This pre-established threshold system allows for rapid deployment of demand response measures when thresholds are approached, without waiting for capacity constraints to materialize.
3Reliability
If peak demand thresholds are set based on production capacity, then utilities can prevent resource shortages, but this approach does not enable proactive energy efficiency optimization
Solution Approach 1:
The patent changes the threshold-setting parameter from production capacity to historical consumption patterns. This enables proactive energy efficiency optimization by identifying peak demand periods based on actual consumption behavior, allowing utilities to implement demand response programs that reduce energy waste before peak periods occur.
Solution Approach 2:
By pre-calculating peak demand thresholds from historical data, the system enables proactive rather than reactive energy management. Utilities can implement demand response measures in advance of predicted peak periods, optimizing energy efficiency rather than merely preventing shortages.
Data Source
AI summary
The subject disclosure relates to systems and methods for calculating a peak consumption threshold, and for using the peak consumption threshold to determine the likelihood of future peak resource consumption events. In some aspects, methods of the subject technology include steps for receiving resource consumption data for a plurality of utility customers, wherein the resource consumption data comprises an indication of an amount of electric power used by each of the plurality of utility customers, and calculating, using the one or more processors, a plurality of consumption averages for the resource consumption data. In some aspects, the method can further include steps for calculating a peak consumption threshold based on the consumption values for the resource consumption data.