Multi-Source Emissions Forecasting With ML Action Recommendations
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Solution Overview
Problem
Conventional systems are inadequate in monitoring and modeling emissions from multiple sources, leading to inefficient and inaccurate manual tracking and prediction of emissions, especially for small entities with numerous physical emissions sources, and are unable to comply with emerging emissions standards.
Innovation Solution
The system employs a combination of machine-learning models, including a modified gradient descent model and natural language processing, to forecast emissions and generate action recommendations for modifying physical emissions sources, enabling efficient and accurate monitoring and forecasting of emissions across various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking and prediction methods are used for emissions monitoring, then system complexity is reduced, but measurement precision and productivity deteriorate due to inefficiency and inaccuracy
Solution Approach 1:
The patent replaces manual tracking and prediction methods with machine-learning models that automatically forecast emissions from multiple sources. The system uses algorithms to process emissions data, generate forecasts, and provide recommendations without manual intervention, thereby improving measurement precision while managing complexity through automation.
Solution Approach 2:
The emissions monitoring system performs self-service by automatically collecting data from multiple emissions sources, processing it through machine-learning models, generating forecasts, and providing action recommendations without requiring external manual analysis. The system autonomously identifies trends and suggests modifications to reduce emissions.
2Productivity
If conventional systems are used for emissions monitoring, then ease of operation is maintained, but productivity and measurement precision worsen due to inability to handle multiple sources efficiently
Solution Approach 1:
The patent implements a universal emissions monitoring system that handles multiple types of emissions sources (manufacturing processes, transportation, heating/cooling systems) through a single integrated platform. The machine-learning models are designed to process diverse data types and generate comprehensive forecasts, making the system adaptable to various entity sizes and emission profiles without requiring separate manual processes for each source type.
3Measurement precision
If detailed monitoring of numerous emissions sources is implemented, then measurement precision improves, but device complexity and loss of time increase due to data processing requirements
Solution Approach 1:
The system performs preliminary action by continuously collecting and preprocessing emissions data in real-time as it is generated. The machine-learning models are pre-trained to quickly process incoming data streams and generate forecasts without requiring extensive post-collection analysis. This proactive data processing reduces the time needed to produce accurate emissions assessments.
Solution Approach 2:
The patent replaces time-consuming manual data analysis with automated machine-learning models that rapidly process emissions data from multiple sources. The algorithms efficiently identify patterns and generate forecasts in real-time, eliminating the delays associated with manual data collection, consolidation, and analysis while maintaining high measurement precision.
4Reliability
If manual emissions analysis is performed, then device complexity is minimized, but reliability and measurement precision deteriorate due to inability to comply with emerging standards
Solution Approach 1:
The patent implements feedback mechanisms where the machine-learning models continuously monitor emissions data, compare actual emissions against forecasted values and compliance thresholds, and generate real-time alerts when deviations occur. The system provides actionable recommendations for correcting deviations and maintaining compliance, creating a closed-loop system that continuously improves reliability through automated monitoring and adjustment suggestions.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications. In additional embodiments, the disclosed system tracks emissions of the entity during the future time period and generate additional action recommendations in response to detecting deviations from forecasted emissions production.