Energy Window Forecasting for Renewable-Aware Power Use
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Solution Overview
Problem
Consumers are unaware of the source of electricity being provided by the electrical grid, which can lead to increased carbon emissions and greenhouse gas production, and existing systems lack real-time optimization to encourage renewable energy use.
Innovation Solution
A computing service that receives real-time data from multiple sources to generate energy optimization suggestions, using machine learning models to forecast energy time intervals where renewable energy is available and cheaper, and presents these as 'energy windows' to users via user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If consumers use electricity without awareness of its source, then energy consumption continues unchanged, but carbon emissions and greenhouse gas production increase
Solution Approach 1:
The patent introduces an intermediary system (energy optimization service, computing system, user interface) that mediates between the consumer and the energy grid. This intermediary provides real-time information about energy sources and recommends optimization actions, enabling consumers to make informed decisions without directly interacting with the complex grid infrastructure. The intermediary translates raw energy data into actionable insights for consumers.
Solution Approach 2:
The system implements feedback loops where energy consumption data is collected, analyzed, and returned to consumers as recommendations. The feedback mechanism provides consumers with information about their current energy usage patterns, the carbon intensity of their energy sources, and specific actions they can take to reduce emissions. This continuous feedback enables consumers to adjust behavior based on real-time conditions.
2Productivity
If real-time energy optimization systems are implemented, then renewable energy usage increases, but system complexity and data processing requirements increase
Solution Approach 1:
The energy optimization service performs multiple functions within a single system architecture: collecting energy data from multiple sources, analyzing consumption patterns, generating optimization recommendations, and providing user interfaces. This multi-functional approach consolidates what could be separate complex systems into one integrated service, reducing overall system complexity while maintaining comprehensive functionality.
Solution Approach 2:
The system employs machine learning models that automatically analyze energy data and generate optimization recommendations without requiring manual intervention. The automated analysis and recommendation generation reduce the need for complex human-operated control systems, allowing the system to self-manage the complexity of real-time optimization while maximizing renewable energy utilization.
3Productivity
If energy data from multiple sources is collected and analyzed, then energy production optimization improves, but data processing time and computational resources increase
Solution Approach 1:
The system collects and pre-processes energy data from multiple sources in advance, organizing and validating data before it is needed for optimization decisions. By performing preliminary data collection and preparation, the system reduces the computational burden during real-time optimization, enabling faster processing when decisions are needed without sacrificing the comprehensiveness of multi-source data analysis.
Data Source
AI summary
Techniques are described for generating energy interval notifications. An example can include a computing system configured to access generation source information, grid conditions information, and price information. The computing system can generate forecasted values based at least in part on historical information, where the forecasted values are generated for a first time interval. The computing system can also determine an energy forecast for the first time interval, where the energy forecast is based at least in part on the forecasted rate and the forecasted values.