Onetrust, LLC

Akhil Dandamudi

Madan Avadhani

Casey Hill

Sohail Jooma

Phil Redman

Daniel B Quintas

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for modifying physical emissions sources based on a plurality of simulations of different scenarios utilizing a modified gradient descent model. Specifically, the disclosed system utilizes the modified gradient descent model to generate emissions value modifications for physical emissions sources corresponding to an entity based on a set of constraints and target emissions values. The disclosed system runs a plurality of simulations to generate modified target emissions values, utilizing the modified gradient descent model, by modifying source attributes of the physical emissions sources according to a plurality of probability distributions representing source attributes of the physical emissions sources. The disclosed system then compares the initial target emissions values to the modified target emissions values determined from the simulations to generate action recommendations for modifying the physical emissions sources.

Generating action recommendations for modifying physical emission sources based on many simulations of different scenarios utilizing a modified gradient descent model

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.

Generating forecasted emissions value modifications and monitoring for physical emissions sources utilizing machine-learning models

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations modifying physical emissions sources of an entity based on past and modeled emissions for the entity. Specifically, the disclosed system monitors emissions produced by an entity by determining a number of emissions sources corresponding to an entity and a plurality of emissions values for the emissions sources. Additionally, the disclosed system determines a plurality of constraints corresponding to the entity. The disclosed system also determines goals for the entity including target emissions values. The disclosed system utilizes a modified gradient descent model to iteratively adjust emissions values for the physical emissions sources to obtain the target emissions values according to the constraints. The disclosed system generates action recommendations for modifying the physical emissions sources utilizing the modified gradient descent model and provides the action recommendations for display within a graphical user interface.

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Atlanta, GA, United States of America

Akhil Dandamudi