Dynamic Loadshape Forecasting via Iterative Adjustment
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Solution Overview
Problem
Existing loadshape forecasting techniques fail to accurately predict electrical demand in utility systems that support dynamic power pricing due to nonlinear variations caused by changing consumer behavior in response to real-time price adjustments.
Innovation Solution
A system that uses input signals from smart meters, an inferential model, and a Fourier-based decomposition-and-reconstruction technique to project and optimize loadshapes, accounting for nonlinear effects from dynamic pricing by iteratively adjusting projections based on historical load-related parameters and weather data, and controlling electricity supply accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing loadshape forecasting techniques are used, then the forecasting process is simple, but the prediction accuracy deteriorates due to nonlinear variations from dynamic pricing
Solution Approach 1:
The forecasting system dynamically adapts to changing conditions by incorporating real-time dynamic pricing signals and consumer behavior responses. The model transitions from static historical aggregation to dynamic simulation that captures nonlinear variations in demand patterns as prices fluctuate, allowing accurate prediction under dynamic pricing conditions
Solution Approach 2:
The system incorporates feedback loops where dynamic pricing signals influence consumer behavior, which in turn affects aggregate demand, which then feeds back into price adjustments. This feedback mechanism is explicitly modeled to capture the nonlinear interactions between pricing, consumer response, and demand aggregation, improving prediction accuracy
2Ease of operation
If dynamic power pricing is implemented, then consumer cost optimization is improved, but demand prediction accuracy deteriorates due to feedback effects
Solution Approach 1:
The system explicitly models the feedback loop created by dynamic pricing where price signals influence consumer behavior and aggregate demand. By incorporating this feedback mechanism into the forecasting model, the system accurately captures nonlinear demand variations that result from consumers optimizing their costs in response to dynamic prices
Solution Approach 2:
The forecasting system acts as an intermediary that processes both dynamic pricing signals and consumer response patterns to predict aggregate demand. It mediates between the complex interactions of individual consumer decisions and overall system demand, translating micro-level behavioral responses into macro-level demand predictions
3Measurement precision
If aggregate demand forecasting is performed without considering individual consumer behavior, then the forecasting process is simple, but the accuracy deteriorates under dynamic pricing
Solution Approach 1:
The system segments the aggregate demand into individual consumer-level components, allowing it to model how each consumer responds to dynamic pricing signals. By decomposing aggregate demand into constituent consumer behaviors and then re-aggregating them, the system captures nonlinear effects that would be lost in direct aggregation while maintaining computational feasibility
Data Source
AI summary
During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, which gather electrical usage data from customers of the utility system. The system uses the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system. Next, the system identifies a closest time period in a database containing recent empirically obtained load-related parameters for the utility system, wherein the load-related parameters in the closest time period are closest to a present set of load-related parameters for the utility system. The system then iteratively adjusts the projected loadshapes based on changes indicated by the load-related parameters in the closest time period until a magnitude of adjustments falls below a threshold. Finally, the system predicts electricity demand for the utility system based on the projected loadshapes.