Uncertainty-Aware Forecasting for Emissions Planning and Control
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Solution Overview
Problem
Existing systems fail to effectively plan and optimize emissions control to achieve net zero emissions by a planned date due to uncertainty in future operations and lack of consideration for uncertain variables, leading to inefficiencies and potential failure to meet sustainability commitments.
Innovation Solution
A computer-implemented method using forecasting models to generate long-term emissions optimization plans and short-term emissions control strategies, incorporating operational data classification and uncertainty predictions to guide emissions optimization actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual planning by workers with specialized domain knowledge is used, then domain expertise is leveraged, but the plans become error-prone and unoptimized due to human limitations
Solution Approach 1:
The patent replaces manual planning processes with an automated machine learning-based system. The system ingests operational data from multiple sources, classifies variables, generates forecasting models, and produces optimized emissions control plans automatically, eliminating human errors and improving both accuracy and efficiency simultaneously
Solution Approach 2:
The system transforms planning from qualitative manual assessment to quantitative data-driven analysis. By converting operational data into structured variables and using machine learning models to process them, the system achieves superior planning accuracy and efficiency compared to manual methods
2Reliability
If future operations are planned without considering uncertain variables, then planning is simplified, but net zero emissions targets cannot be met due to lack of visibility into future conditions
Solution Approach 1:
The system performs preliminary classification of variables into certain and uncertain categories before planning. By identifying uncertain variables in advance and incorporating them into the forecasting models, the system prepares for future uncertainties rather than reacting to them, ensuring reliable emissions target achievement
Solution Approach 2:
The system continuously monitors operational data and uses it to refine forecasting models. This feedback loop allows the system to adapt to changing conditions and improve its predictions of uncertain variables, enabling more reliable emissions planning while managing complexity through automated iterative refinement
3Productivity
If only certain variables are considered in planning, then planning is more straightforward, but emissions control becomes ineffective due to omission of uncertain factors
Solution Approach 1:
The system segments operational data into distinct variable categories (certain and uncertain). By separating these types of variables and applying different handling approaches to each, the system maintains planning clarity while incorporating the necessary complexity of uncertain factors through specialized forecasting models
Solution Approach 2:
The system introduces forecasting models as intermediary components that process uncertain variable information. These models act as mediators between raw operational data and final planning decisions, transforming uncertain information into probabilistic predictions that can be incorporated into emissions control strategies without overwhelming complexity
Data Source
AI summary
Embodiments described herein relate to constrained emissions control, optimization, and planning. An example method may include receiving operational data associated with one or more assets. The method may include classifying the operational data as first variables and second variables. The method may include generating, based at least in part on the operational data, one or more forecasting models that provide one or more predictions associated with uncertainty of the second variables. The method may include generating, based at least in part on applying the operational data and the one or more predictions to an optimization model, a long-term emissions optimization plan. The method may include generating, based at least in part on the long-term emissions optimization plan, a short-term emissions control. The method may include initiating performance of one or more emissions optimization actions based at least in part on the long-term emissions optimization plan or the short-term emissions control.