Remote Energy Auditing Using Utility Data and Weather Integration
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Solution Overview
Problem
Power utilities face challenges in accurately gauging on-going and forecasted power generation from photovoltaic fleets and individual systems, and in estimating residential power consumption, due to variability in solar irradiance, incomplete or incorrect photovoltaic system configuration data, and the complexity of residential energy usage patterns.
Innovation Solution
A system and method using a digital computer to analyze building performance and consumer energy consumption by combining total energy load with meteorological data, inferring photovoltaic system configuration specifications, and disaggregating energy loads into component types, enabling remote energy auditing and improved forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote energy auditing is performed using digital computer analysis of utility data, then auditing efficiency and cost-effectiveness are improved, but measurement precision of consumer energy consumption may deteriorate due to lack of direct on-site measurements
Solution Approach 1:
The system creates virtual copies of physical energy audit processes by using digital computer algorithms to replicate on-site measurement functions. Utility data, weather data, and building characteristics are processed through computational models that simulate direct measurement, providing sufficient accuracy for remote auditing without physical presence.
Solution Approach 2:
The digital computer acts as an intermediary between utility data sources and energy consumption analysis. It processes and integrates multiple data sources (utility billing data, weather data, building characteristics) to produce accurate energy consumption estimates, bridging the gap between available data and required insights.
2Measurement precision
If detailed on-site energy audits are conducted, then measurement precision of energy consumption is improved, but loss of time and increased cost are worsened
Solution Approach 1:
The system enables energy auditing to perform itself through automated digital processing. Once the computational model is established, it automatically processes utility data and weather data without requiring continuous human intervention or on-site presence, providing ongoing energy consumption analysis at minimal time cost.
Solution Approach 2:
The system performs preliminary data collection and processing by gathering utility data and building characteristics in advance. This preparation allows rapid energy consumption analysis when needed, eliminating the need for time-consuming on-site audits while maintaining measurement precision through pre-configured computational models.
3Adaptability or versatility
If photovoltaic system configuration data is collected from multiple sources, then adaptability of the system is improved, but difficulty of detecting and measuring accurate power generation is worsened due to incomplete or incorrect data
Solution Approach 1:
The system uses feedback from utility data and weather data to validate and refine photovoltaic power generation measurements. By comparing expected generation (based on weather and system configuration) with actual utility data, the system can detect and correct measurement errors or incomplete configuration information.
Solution Approach 2:
The digital computer system performs multiple functions: it processes utility data, integrates weather data, analyzes building characteristics, and computes energy consumption across different building types and data sources. This multi-functional approach handles diverse photovoltaic system configurations uniformly, reducing measurement difficulty through standardized processing.
Data Source
AI summary
A system and method to analyze building performance without requiring an on-site energy audit or customer input is described. The analysis combines total customer energy load from a power utility with externally-supplied meteorological data to analyze each customer's building performance. Building thermal performance is characterized to produce a rich dataset that the power utility can use in planning and operation, including assessing on-going and forecasted power consumption, and for other purposes, such as providing customers with customized information to inform their energy investment decisions and identifying homes for targeted efficiency funding.