17526183. GENERATING GREENHOUSE GAS EMISSIONS ESTIMATIONS ASSOCIATED WITH LOGISTICS CONTEXTS USING MACHINE LEARNING TECHNIQUES simplified abstract (International Business Machines Corporation)
GENERATING GREENHOUSE GAS EMISSIONS ESTIMATIONS ASSOCIATED WITH LOGISTICS CONTEXTS USING MACHINE LEARNING TECHNIQUES
Organization Name
International Business Machines Corporation
Inventor(s)
Kumar Saurav of Bangalore (IN)
Ranjini Bangalore Guruprasad of Bangalore (IN)
Jagabondhu Hazra of Bangalore (IN)
Manikandan Padmanaban of Bangalore (IN)
Isaac Waweru Wambugu of Nairobi (KE)
GENERATING GREENHOUSE GAS EMISSIONS ESTIMATIONS ASSOCIATED WITH LOGISTICS CONTEXTS USING MACHINE LEARNING TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 17526183 titled 'GENERATING GREENHOUSE GAS EMISSIONS ESTIMATIONS ASSOCIATED WITH LOGISTICS CONTEXTS USING MACHINE LEARNING TECHNIQUES
Simplified Explanation
The patent application describes methods, systems, and computer program products for using machine learning techniques to generate greenhouse gas (GHG) emissions estimations in logistics contexts. Here are the key points:
- Input data related to multiple aspects of at least one logistics context is obtained.
- Contextual features are derived from the input data using data profiling techniques.
- At least one machine learning model related to energy consumption is trained based on the contextual features.
- Energy consumption estimates are generated for logistics implementations by processing data using the trained machine learning model.
- Greenhouse gas emissions estimates are generated based on the energy consumption estimates.
- Automated actions can be performed based on the generated greenhouse gas emissions estimates.
Potential applications of this technology:
- Environmental impact assessment in logistics operations
- Optimization of logistics processes to reduce GHG emissions
- Compliance monitoring and reporting for sustainability initiatives
Problems solved by this technology:
- Lack of accurate and efficient methods for estimating GHG emissions in logistics contexts
- Difficulty in identifying energy consumption patterns and their impact on emissions
- Manual and time-consuming processes for assessing environmental impact in logistics
Benefits of this technology:
- Accurate estimation of GHG emissions in logistics operations
- Improved understanding of energy consumption patterns and their relationship to emissions
- Automated actions based on emissions estimates can drive sustainability efforts and reduce environmental impact.
Original Abstract Submitted
Methods, systems, and computer program products for generating GHG emissions estimations associated with logistics contexts using machine learning techniques are provided herein. A computer-implemented method includes obtaining input data related to multiple aspects of at least one logistics context; deriving contextual features from the input data by processing the input data using data profiling techniques; training at least one machine learning model related to energy consumption based on the contextual features; generating at least one energy consumption estimate attributed to at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generating at least one greenhouse gas emissions estimate attributed to the at least one logistics implementation based on the at least one energy consumption estimate; and performing automated actions based on the at least one generated greenhouse gas emissions estimate.
