PV Specification Inference From Net Metering Load Data
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Solution Overview
Problem
Utilities face challenges in accurately forecasting load demand due to incomplete or inaccurate PV system specifications from consumer-reported data, leading to uncertainties in load forecasting and increased operational costs from dual meter installations.
Innovation Solution
A method and system that infers PV system specifications using trustworthy data such as location and net load data from NEM meters, employing a digital computer to estimate system specifications like azimuth, tilt, and size, minimizing residual errors to improve forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If consumer-reported PV system specifications are used, then device complexity is reduced, but measurement precision deteriorates due to incomplete or inaccurate data
Solution Approach 1:
The system uses actual net load data measured by the NEM meter as feedback to iteratively refine and infer the PV system specifications. By comparing measured net load against simulated production based on initial specifications, the system adjusts parameters like system size, tilt, and azimuth to minimize errors, thereby improving measurement precision without adding physical complexity
Solution Approach 2:
The patent replaces the need for complex dual-meter mechanical/electrical measurement systems with a computational inference system. Instead of physically installing and maintaining two meters to directly measure PV production, the system uses digital processing of net load data combined with solar production simulation to infer specifications, reducing device complexity while maintaining or improving accuracy
2Measurement precision
If dual meters are installed to measure total PV production, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary measurement function from the dual-meter system. Instead of installing two separate meters to measure both net load and total PV production, the system retains only the NEM meter for net load measurement and extracts PV production information computationally by simulating production based on inferred specifications and comparing with measured net load data
Solution Approach 2:
The system introduces a computational model as an intermediary between the physical NEM meter and the required PV production data. The simulation model acts as a mediator that translates measured net load data into inferred PV production characteristics, eliminating the need for direct physical measurement by a second meter while maintaining measurement precision
3Ease of operation
If consumer-reported PV system specifications are used, then ease of operation is improved, but reliability deteriorates due to data inaccuracies
Solution Approach 1:
The system continuously refines reliability by using feedback from actual measured net load data. Initial consumer-reported specifications serve as a starting point, but the system iteratively adjusts these specifications to minimize the difference between simulated and actual net load measurements, thereby improving forecasting reliability while maintaining the simplicity of initial data collection
Solution Approach 2:
The system performs preliminary computational validation and refinement of consumer-reported specifications before finalizing the PV system profile. By pre-processing and verifying the reported data against physical constraints and measured net load patterns, the system ensures reliability is improved before the data is used for forecasting, while still maintaining ease of operation in the initial data collection phase
Data Source
AI summary
A system and method for inferring photovoltaic (PV) system specifications are described. A consumer PV system's location and net load data recorded through net metering are retrieved. For each discrete period, a time of peak PV production and a magnitude of minimum net load for a representative day are found, and base loads are estimated. Plane-of-array irradiance (POAI) using clear sky global horizontal irradiance for azimuth and tilt combinations is produced. Azimuth, tilt, and system rating combinations based on the magnitude of minimum net load and the base load over each discrete period are created. A lowest error metric among the azimuth, tilt and system rating combinations is found, by finding the minimum of the combined residual errors in the time of peak PV production and the time of maximum POAI and residual errors in the magnitude of minimum net load plus the base load and magnitude of maximum POAI.