Load Forecasting With Tuned Weather Data for Utility Grids
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Solution Overview
Problem
Current load forecasting in utility grids faces challenges due to inaccurate weather data and the lack of technological solutions that can scale across multiple markets, leading to inefficiencies and increased costs in managing energy demand.
Innovation Solution
A system utilizing artificial intelligence and load-sensitive weather instruments to generate tuned weather data, which is then used to forecast energy loads within utility grids, taking into account various environmental conditions and grid specifications, thereby improving forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic weather data from standard weather stations (e.g., airports) is used for load forecasting, then the weather data is readily available and easy to obtain, but the forecasting accuracy deteriorates because the data does not reflect real-world load profiles and experiences low turbulence areas that do not drive power demand variability
Solution Approach 1:
The patent applies local quality by deploying weather instruments at specific locations that are sensitive to load-driving weather conditions rather than using generic airport weather stations. The system identifies and instruments locations where weather variations actually drive power demand changes, making the weather data locally relevant to the load profile being forecasted.
Solution Approach 2:
The patent introduces an intermediary layer between generic weather data and load forecasting by using load-sensitive weather instruments that specifically measure weather parameters known to drive power demand. This intermediary measurement system translates general weather conditions into load-relevant weather data.
2Measurement precision
If load-sensitive weather instruments are deployed at optimally sited locations to capture load-driving weather conditions, then the forecasting accuracy improves, but the system complexity and cost of deployment increases
Solution Approach 1:
The patent segments the weather measurement function by deploying distributed weather instruments at multiple strategically selected locations rather than relying on a single generic weather station. This segmentation allows the system to capture spatial variations in weather conditions that drive load at different geographic areas.
Solution Approach 2:
The system employs automated selection and deployment methods that reduce manual intervention in identifying optimal instrument locations. The methodology includes automated analysis of historical load and weather data to identify load-sensitive locations, reducing the complexity of manual site selection and deployment planning.
3Ease of manufacture
If traditional demand forecasting models are used that rely on generic weather data, then the model implementation is simple and cost-effective, but the forecast performance deteriorates leading to misinformed market participant behaviors and inefficient grid optimization
Solution Approach 1:
The patent changes the input parameters of the forecasting model from generic weather data to load-sensitive weather data collected from strategically located instruments. This parameter change transforms the model's input quality, enabling more accurate forecasts while maintaining the same basic forecasting framework.
Solution Approach 2:
The patent replaces the mechanical approach of using readily available but inaccurate weather data with an automated system that selects and collects weather data from optimally located instruments. This substitution uses computational methods to identify and deploy measurement infrastructure, replacing manual data collection approaches.
Data Source
AI summary
The present description relates to systems and methods for load forecasting for improved energy use forecasting based on tuned weather data. The system includes a network of load sensitive weather instruments for producing tuned weather data, and a processor linked to the network of load sensitive weather instruments and receiving the tuned weather data. The processor obtains the tuned weather data from the network of load sensitive weather instruments and is configured to analyse a utility grid specification and correlate the utility grid specification with the tuned weather data to forecast the energy load within the grid