Machine learning-based resource prediction and optimization — C3.Ai, Inc. (US20240403776A1)

ABSTRACT

Abstract

Aspects of this disclosure are directed to enterprise systems and methods that provide machine learning and artificial intelligence (AI) driven software that generates baseline predictions and optimizations for production capacity to reduce waste and harmful byproducts. Baseline predictions can include resource baseline predictions that can help estimate (or, predict) how much lower or higher an asset's resource inputs (e.g., fuel) and/or outputs (e.g., emissions) could be in comparison to the asset's current resource inputs and/or outputs. AI generated baseline predictions can be broken down into several different levels (e.g., facility level down to the asset level) so that it is clear where the most significant opportunities for resource savings lie, and the system can perform optimizations (e.g., trigger corrective actions) to achieve those resource savings.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional Patent Application Ser. 63/505,680, filed Jun. 1, 2023, and entitled “Machine Learning-based Resource Prediction and Optimization,” which is hereby incorporated by reference herein.

TECHNICAL FIELD

This disclosure pertains to artificial intelligence and machine learning optimization solutions. More specifically, this disclosure pertains to enterprise systems and methods for machine learning-based resource prediction and optimization.

BACKGROUND

Under traditional approaches, computing systems are unable to quantify resource reduction potential (or gap to reduction potential) based on historical performance under different scenarios, or otherwise. Traditional computing systems are also unable to automatically or accurately identify drivers that correlate with resource outputs (e.g., greenhouse gas emissions). Traditional computing systems are incapable of scaling resource optimization approaches across many different facilities (e.g., factories) that may each contain many different assets (e.g., physical equipment).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a diagram of an example network system for machine learning-based resource prediction and optimization according to some embodiments.

FIG. F 2 depicts a diagram of an example system flow of a machine learning-based resource prediction and optimization system using hierarchical aggregation to generate accurate results at the facility level when there are different types of assets and/or dependencies according to some embodiments.

FIG. 3 depicts a diagram of an example machine learning-based resource prediction and optimization system according to some embodiments.

FIG. 4 depicts a diagram of an example system architecture and flow (e.g., of a machine learning-based resource prediction and optimization system and/or forecasting module) integrating Temporal Fusion Transformers (TFT) into the machine learning processes of a machine learning-based resource prediction and optimization system according to some embodiments.

FIG. 5 A depicts a flowchart of an example of a method of resource baseline prediction according to some embodiments.

FIG. 5 B depicts a flowchart of an example of a method of adjusting an asset based on predicted values and threshold values according to some embodiments.

FIG. 5 C depicts a flowchart of an example of a method of artificial intelligence-driven forecasting according to some embodiments.

FIG. 5 D depicts a flowchart of an example of a method of cohort baseline prediction according to some embodiments.

FIG. 6 depicts a flowchart of an example of a method of cohort baseline prediction according to some embodiments.

FIG. 7 depicts a flowchart of an example of a method of generative artificial intelligence-based resource baseline prediction according to some embodiments.

FIG. 8 depicts a diagram of an example method of machine learning-based resource prediction and optimization at the facility-level according to some embodiments.

FIG. 9 depicts a flowchart of an example of a method of training a machine learning model for resource baseline prediction according to some embodiments.

FIG. 10 depicts a flowchart of an example of a method of hierarchical aggregation to generate accurate results at the facility level when there are different types of assets and/or dependencies according to some embodiments.

FIG. 11 depicts a flowchart of an example of a method of generative artificial intelligence-based resource prediction and optimization according to some embodiments.

FIG. 12 A depicts a diagram of example cohort baselining pipelines according to some embodiments.

FIG. 12 B depicts a diagram of example system flow and output that uses machine learning-predicted target (e.g., ideal/optimal) baseline for asset performance benchmarking relative to asset peers.

FIG. 13 depicts a flowchart of an example of a method of operation of an orchestrator module according to some embodiments.

FIG. 14 is a diagram of an example computer system for implementing the features disclosed herein according to some embodiments.

DETAILED DESCRIPTION

Managing and mitigating resource consumption is a top priority across the world. Exacerbating the situation, organizations often cannot accurately calculate the resources that they consume (e.g., fuel) or byproducts generated (e.g., emissions), let alone determine how to improve the efficiency of their operations to reduce the amount of resources consumed and produced. For example, an organization (e.g., enterprise) may have many different facilities (e.g., factories) spread across several different continents, and each of the factories may consume and produce an indeterminate amount of resources. Each of the factories may also include many different assets (e.g., physical equipment, such as furnaces), and each of those assets may also individually consume and produce an indeterminate amount of resources and byproducts. The systems and methods described herein can not only accurately track the amount of resources consumed and byproducts produced, but they also provide analytics and artificial intelligence insights that can accurately predict how to improve the efficiency of their operations and reduce resource consumption and byproduct generation. Moreover, the systems and methods described herein can do this at the organizational level, the facility level, all the way down to the individual asset level. For example, the systems and methods described herein can (1) determine an overall resource consumption and production of organizations, factories, and assets, (2) determine which factories and assets are inefficient, (3) diagnose the issues causing the inefficiencies, and (4) recommend and/or perform corrective actions (e.g., modifying configurations of factories or assets) to bring them to optimal or acceptable levels.

In one example, a computing system provides artificial intelligence-driven software application baseline predictions to determine expected and optimal resource utilization (e.g., consumption, production) for organizations, factories, and assets. Expected resource utilization can include predicted values indicating the amount of resources a facility or asset should consume or amount of byproducts generated based on the data (e.g., time series data) of that facility or asset. For example, given feed rates, a type of fuel used by an asset, and tensor encapsulating other measured operating conditions in real time such as sensors measuring temperature, pressure, speed, etc. the artificial intelligence software application can predict the amount of resources that the asset should consume or the amount of byproducts that should be generated under current conditions. Optimal resource utilization can include predicted values indicating a maximum efficiency (e.g., based on a predicted lowest amount of resource consumption or production) of a facility or asset based on the data of that facility or asset. These baseline predictions can help estimate (or, predict) how much lower resource outputs (e.g., emissions) in comparison to current resource outputs via analysis of the asset's tensor of operating condition measurements during a specified baseline period when the asset was performing well or as intended (such as directly after maintenance or using a fuel type with higher conversion efficiency or lower carbon weight). Selection of the baseline period defines the benchmark scenario represented by the prediction series. Baseline predictions, as well as the other predictions discussed herein, can be provided at several different levels of granularity (e.g., organizational, facility, asset) so that it is clear where the most significant opportunities for resource savings lie across a massive hierarchy of assets. This approach enables rapid prioritization of opportunities across global operations-which is intractable using manual methods which do not consider the massive collection of tensors signifying each asset's operating conditions over time and space.

Example aspects include machine learning based predictions to compare resource inputs and outputs at all levels of a facility against baseline predictions to perform resource optimizations over time. The predictions provide resource savings opportunities, such as where fuel consumption and greenhouse gas emissions can be reduced. Resource predictions can be directed to resources such as greenhouse gases, combusted fossil fuels, waste, compressed air, and derived resources, among others. An example aspect includes hierarchical aggregations for its forecasts, from individual assets up to the facility level, allowing more accurate forecasts than a non-hierarchical solution. The hierarchical aggregation capability reinforces in-depth analysis across levels from asset to entire facility. It enables a powerful level of accuracy to address output efficiency concerns in a novel and adaptive ways that traditional approaches fail at by summing outputs and inputs of the facility assets. This multi-level capability, combined with the machine learning prediction capabilities for outputs includes inspection and explainability of the analysis to drill down from facility to individual assets of interest to provide new and robust predictions.

The computing system provides artificial intelligence-driven cohort baseline predictions to determine ideal performance (e.g., resource consumption, resource production, efficiency) of facilities and assets relative to peer facilities and assets. Each ideal value in the prediction series may be the optimum (e.g., typically minimum or maximum; aggregation function depends on target variable) of inferred expected values across all peer assets in the cohort for a single tensor of asset operating conditions representing the studied asset's state at a particular timestamp. This can provide enhanced opportunity identification because predictions for various facilities or assets can be biased by the data of those facilities and assets. For example, a particular asset may be operating efficiently based on the expected baseline prediction for that asset, but that efficiency may not tell whole the story. For example, the asset may be located in one area of a facility and another asset of the same type may be located in another area of the facility or feature recently retrofitted components of superior design and quality. By performing a cohort analysis, the computing system may determine that the given asset is actually inefficient relative to its peers while it is relatively efficient when compared to its own baseline period which can be due to the location of the asset in the factory or lack of retrofitted parts rather than some problem with the asset itself. Detection of these discrepancies across assets is enabled via operationally-aware benchmarking across both time and space—automatically benchmarking across the potentially high dimensional vector space of operating conditions among peer assets within an expertly or automatically defined cohort via clustering. Accordingly, by reducing asset-specific bias, the computing system can greatly increase system accuracy in detecting performance issues and outliers (e.g., increase comprehensiveness of optimization recommendations).

As described herein, the artificial intelligence-driven baseline predictions provide organizations and facilities greater insight into where outlier sources of waste are while the detailed system optimizes assets to try to maintain an efficient output baseline. Example implementations focusing on managing resources (e.g., harmful resources) provides a narrowing to its prediction that aids in solving previously addressable problems with a long felt need from the industry. The novel systems and techniques disclosed herein provides benefit to improve efficiency and optimization of how much production capacity can be fit inside waste limits. This not only improves yield and cost savings but also has positive environmental benefits. AI based predictions can provide recommendations in real-time to generate a time-series of control actions or adjustments to optimize large scale operations that have a multitude of assets across a multiple facilities.

FIG. 1 depicts a diagram 100 of an example network system for machine learning-based resource prediction and optimization according to some embodiments. In the example of FIG. 1 , the system includes a machine learning-based resource prediction and optimization system 102 , facilities 103 - 1 to 103 -N (individually, the facility 103 , collectively, the facilities 103 ), assets 104 - 1 to 104 -N (individually, the asset 104 , collectively, the assets 104 ), assets 106 - 1 to 106 -N (individually, the asset 106 , collectively, the assets 106 ), data sources 108 - 1 to 108 -N (individually, the data source 108 , collectively, the data sources 108 ), assets 110 - 1 to 110 -N (individually, the asset 110 , collectively, the assets 110 ), and a communication network 112 .

The machine learning-based resource predi

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional Patent Application Ser. 63/505,680, filed Jun. 1, 2023, and entitled “Machine Learning-based Resource Prediction and Optimization,” which is hereby incorporated by reference herein.

TECHNICAL FIELD

This disclosure pertains to artificial intelligence and machine learning optimization solutions. More specifically, this disclosure pertains to enterprise systems and methods for machine learning-based resource prediction and optimization.

BACKGROUND

Under traditional approaches, computing systems are unable to quantify resource reduction potential (or gap to reduction potential) based on historical performance under different scenarios, or otherwise. Traditional computing systems are also unable to automatically or accurately identify drivers that correlate with resource outputs (e.g., greenhouse gas emissions). Traditional computing systems are incapable of scaling resource optimization approaches across many different facilities (e.g., factories) that may each contain many different assets (e.g., physical equipment).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a diagram of an example network system for machine learning-based resource prediction and optimization according to some embodiments.

FIG. F 2 depicts a diagram of an example system flow of a machine learning-based resource prediction and optimization system using hierarchical aggregation to generate accurate results at the facility level when there are different types of assets and/or dependencies according to some embodiments.

FIG. 3 depicts a diagram of an example machine learning-based resource prediction and optimization system according to some embodiments.

FIG. 4 depicts a diagram of an example system architecture and flow (e.g., of a machine learning-based resource prediction and optimization system and/or forecasting module) integrating Temporal Fusion Transformers (TFT) into the machine learning processes of a machine learning-based resource prediction and optimization system according to some embodiments.

FIG. 5 A depicts a flowchart of an example of a method of resource baseline prediction according to some embodiments.

FIG. 5 B depicts a flowchart of an example of a method of adjusting an asset based on predicted values and threshold values according to some embodiments.

FIG. 5 C depicts a flowchart of an example of a method of artificial intelligence-driven forecasting according to some embodiments.

FIG. 5 D depicts a flowchart of an example of a method of cohort baseline prediction according to some embodiments.

FIG. 6 depicts a flowchart of an example of a method of cohort baseline prediction according to some embodiments.

FIG. 7 depicts a flowchart of an example of a method of generative artificial intelligence-based resource baseline prediction according to some embodiments.

FIG. 8 depicts a diagram of an example method of machine learning-based resource prediction and optimization at the facility-level according to some embodiments.

FIG. 9 depicts a flowchart of an example of a method of training a machine learning model for resource baseline prediction according to some embodiments.

FIG. 10 depicts a flowchart of an example of a method of hierarchical aggregation to generate accurate results at the facility level when there are different types of assets and/or dependencies according to some embodiments.

FIG. 11 depicts a flowchart of an example of a method of generative artificial intelligence-based resource prediction and optimization according to some embodiments.

FIG. 12 A depicts a diagram of example cohort baselining pipelines according to some embodiments.

FIG. 12 B depicts a diagram of example system flow and output that uses machine learning-predicted target (e.g., ideal/optimal) baseline for asset performance benchmarking relative to asset peers.

FIG. 13 depicts a flowchart of an example of a method of operation of an orchestrator module according to some embodiments.

FIG. 14 is a diagram of an example computer system for implementing the features disclosed herein according to some embodiments.

DETAILED DESCRIPTION

Managing and mitigating resource consumption is a top priority across the world. Exacerbating the situation, organizations often cannot accurately calculate the resources that they consume (e.g., fuel) or byproducts generated (e.g., emissions), let alone determine how to improve the efficiency of their operations to reduce the amount of resources consumed and produced. For example, an organization (e.g., enterprise) may have many different facilities (e.g., factories) spread across several different continents, and each of the factories may consume and produce an indeterminate amount of resources. Each of the factories may also include many different assets (e.g., physical equipment, such as furnaces), and each of those assets may also individually consume and produce an indeterminate amount of resources and byproducts. The systems and methods described herein can not only accurately track the amount of resources consumed and byproducts produced, but they also provide analytics and artificial intelligence insights that can accurately predict how to improve the efficiency of their operations and reduce resource consumption and byproduct generation. Moreover, the systems and methods described herein can do this at the organizational level, the facility level, all the way down to the individual asset level. For example, the systems and methods described herein can (1) determine an overall resource consumption and production of organizations, factories, and assets, (2) determine which factories and assets are inefficient, (3) diagnose the issues causing the inefficiencies, and (4) recommend and/or perform corrective actions (e.g., modifying configurations of factories or assets) to bring them to optimal or acceptable levels.

In one example, a computing system provides artificial intelligence-driven software application baseline predictions to determine expected and optimal resource utilization (e.g., consumption, production) for organizations, factories, and assets. Expected resource utilization can include predicted values indicating the amount of resources a facility or asset should consume or amount of byproducts generated based on the data (e.g., time series data) of that facility or asset. For example, given feed rates, a type of fuel used by an asset, and tensor encapsulating other measured operating conditions in real time such as sensors measuring temperature, pressure, speed, etc. the artificial intelligence software application can predict the amount of resources that the asset should consume or the amount of byproducts that should be generated under current conditions. Optimal resource utilization can include predicted values indicating a maximum efficiency (e.g., based on a predicted lowest amount of resource consumption or production) of a facility or asset based on the data of that facility or asset. These baseline predictions can help estimate (or, predict) how much lower resource outputs (e.g., emissions) in comparison to current resource outputs via analysis of the asset's tensor of operating condition measurements during a specified baseline period when the asset was performing well or as intended (such as directly after maintenance or using a fuel type with higher conversion efficiency or lower carbon weight). Selection of the baseline period defines the benchmark scenario represented by the prediction series. Baseline predictions, as well as the other predictions discussed herein, can be provided at several different levels of granularity (e.g., organizational, facility, asset) so that it is clear where the most significant opportunities for resource savings lie across a massive hierarchy of assets. This approach enables rapid prioritization of opportunities across global operations-which is intractable using manual methods which do not consider the massive collection of tensors signifying each asset's operating conditions over time and space.

Example aspects include machine learning based predictions to compare resource inputs and outputs at all levels of a facility against baseline predictions to perform resource optimizations over time. The predictions provide resource savings opportunities, such as where fuel consumption and greenhouse gas emissions can be reduced. Resource predictions can be directed to resources such as greenhouse gases, combusted fossil fuels, waste, compressed air, and derived resources, among others. An example aspect includes hierarchical aggregations for its forecasts, from individual assets up to the facility level, allowing more accurate forecasts than a non-hierarchical solution. The hierarchical aggregation capability reinforces in-depth analysis across levels from asset to entire facility. It enables a powerful level of accuracy to address output efficiency concerns in a novel and adaptive ways that traditional approaches fail at by summing outputs and inputs of the facility assets. This multi-level capability, combined with the machine learning prediction capabilities for outputs includes inspection and explainability of the analysis to drill down from facility to individual assets of interest to provide new and robust predictions.

The computing system provides artificial intelligence-driven cohort baseline predictions to determine ideal performance (e.g., resource consumption, resource production, efficiency) of facilities and assets relative to peer facilities and assets. Each ideal value in the prediction series may be the optimum (e.g., typically minimum or maximum; aggregation function depends on target variable) of inferred expected values across all peer assets in the cohort for a single tensor of asset operating conditions representing the studied asset's state at a particular timestamp. This can provide enhanced opportunity identification because predictions for various facilities or assets can be biased by the data of those facilities and assets. For example, a particular asset may be operating efficiently based on the expected baseline prediction for that asset, but that efficiency may not tell whole the story. For example, the asset may be located in one area of a facility and another asset of the same type may be located in another area of the facility or feature recently retrofitted components of superior design and quality. By performing a cohort analysis, the computing system may determine that the given asset is actually inefficient relative to its peers while it is relatively efficient when compared to its own baseline period which can be due to the location of the asset in the factory or lack of retrofitted parts rather than some problem with the asset itself. Detection of these discrepancies across assets is enabled via operationally-aware benchmarking across both time and space—automatically benchmarking across the potentially high dimensional vector space of operating conditions among peer assets within an expertly or automatically defined cohort via clustering. Accordingly, by reducing asset-specific bias, the computing system can greatly increase system accuracy in detecting performance issues and outliers (e.g., increase comprehensiveness of optimization recommendations).

As described herein, the artificial intelligence-driven baseline predictions provide organizations and facilities greater insight into where outlier sources of waste are while the detailed system optimizes assets to try to maintain an efficient output baseline. Example implementations focusing on managing resources (e.g., harmful resources) provides a narrowing to its prediction that aids in solving previously addressable problems with a long felt need from the industry. The novel systems and techniques disclosed herein provides benefit to improve efficiency and optimization of how much production capacity can be fit inside waste limits. This not only improves yield and cost savings but also has positive environmental benefits. AI based predictions can provide recommendations in real-time to generate a time-series of control actions or adjustments to optimize large scale operations that have a multitude of assets across a multiple facilities.

FIG. 1 depicts a diagram 100 of an example network system for machine learning-based resource prediction and optimization according to some embodiments. In the example of FIG. 1 , the system includes a machine learning-based resource prediction and optimization system 102 , facilities 103 - 1 to 103 -N (individually, the facility 103 , collectively, the facilities 103 ), assets 104 - 1 to 104 -N (individually, the asset 104 , collectively, the assets 104 ), assets 106 - 1 to 106 -N (individually, the asset 106 , collectively, the assets 106 ), data sources 108 - 1 to 108 -N (individually, the data source 108 , collectively, the data sources 108 ), assets 110 - 1 to 110 -N (individually, the asset 110 , collectively, the assets 110 ), and a communication network 112 .

The machine learning-based resource prediction and optimization system 102 may function to predict resource baseline variable values for the target variable conveying asset performance (or, simply, resource baseline values) and predict cohort resource baseline values for

assets

104 , 106 , 110 and facilities 103 , to accurately determine resource utilization and operational efficiency at the organizational level, the facility level, and the asset level. The machine learning-based resource prediction and optimization system 102 may facilitate an increase or reduction of many different resources, byproducts, and associated financial costs in the real world or in a simulated environment. For example, the machine learning-based resource prediction and optimization system 102 may recommend or perform (e.g., trigger) corrective setpoint modifications actions based on the baseline predictions to ensure that associated assets are operating at an optimal or acceptable level and performance is maximized (e.g., in terms of emissions and costs reduction). In various embodiments, functionality of the machine learning-based resource prediction and optimization system 102 may be performed by one or more servers (e.g., a cloud-based server) and/or other computing devices. In some implementations, the machine learning-based resource prediction and optimization system 102 may be implemented using a model-driven architecture. Software performance (e.g., speed and latency of the system) is enhanced by exploiting parallelism across traditional servers and/or cores within allocated and virtualized accelerators, properly distributing inference workloads when the number of peer assets, and in turn, the number of models associated with each asset's cohort becomes relatively high.

The facilities 103 may be a part of an organization, and they may each include a variety of different assets 104 - 106 . Assets may also be outside of an organization and/or facility (e.g., the assets 110 ). Such assets may be weather service assets and/or other third-party assets. Assets can include equipment, sensors in the one or more facilities, sensors associated with equipment, sensors outside a facility (e.g., sensors collecting weather data), and the like. Assets 104 - 106 and 110 can include (or, represent) physical assets (e.g., a physical piece of equipment, such as a generator) and/or virtual assets (e.g., an electronic or virtual representation of a piece of equipment or sensor). As used herein, sensors can include, for example, temperature sensors, pressure sensors, speed sensors, rotation sensors, and/or other sensors. Asset sensor data—the majority of which is not resource consumption measurements, but operational measurements as described above—is the most important data source informing baseline predictions, which are typically not incorporated fully, if at all, in status quo methods nor competing solutions lacking large scale ML analysis.

The assets 104 - 106 and 110 can use (e.g., consume) asset inputs 120 - 1 . . . 120 -N and produce asset outputs (or, simply, “outputs”) 130 - 1 . . . 130 -N. For example, asset inputs and output can include physical quantities (e.g., fuel, greenhouse gas emissions), virtual quantities (e.g., simulated fuel, simulated greenhouse gas emissions), and/or other types of sustainability or operational performance indicators. Asset inputs, outputs, and other performance indicators may be individually or collectively referred to as a “KPI” or “KPIs.”

Outputs can include, but are not limited to products, coproducts, and byproducts. Byproducts such as GHG emissions can be automatically quantified in real-time using primary or secondary process activity data (e.g. sensor data, resource consumption data), direct measurement (e.g. directly measured pollutant gas concentrations and flue gas flow by a CEMS or analogous system), mass balance (e.g. computed difference between carbon inputs and outputs across plant's operational boundary) or industry or sector specific models incorporating asset and process specific operational parameters, conversion factors, and formulae. In some embodiments, byproducts are a type of resource.

The assets 106 of the facility 103 can use (e.g., consume) asset inputs 122 - 1 . . . 122 -N and produce outputs 132 - 1 . . . 132 -N. For example, asset inputs and output can include physical quantities (e.g., fuel, greenhouse gas emissions), virtual quantities (e.g., simulated fuel, simulated greenhouse gas emissions), and/or other types of sustainability or operational performance indicators. The assets 110 - 1 . . . 110 -N represent assets located outside of a facility. For example, assets 110 can weather service sensors, third-party sensors, etc. The assets 110 can consume asset inputs 124 - 1 . . . 124 -N and produce outputs 134 - 1 . . . 134 -N.

Resources can include different types of physical and/or virtual resources. For example, resources can include/represent physical or virtual greenhouse gas emissions and/or other types of emissions, electricity consumption/generation, fuels (e.g., combusted and non-combusted fuels), nonfuel hydrocarbon consumption (e.g., feedstock usage of natural gas, other hydrocarbons, etc.), water, waste, compressed air, purchased steam, and/or other types of resource inputs or resource outputs.

In some embodiments, resources may include derived resources (e.g., efficiency). In the example of derived resources, the machine learning-based resource prediction and optimization system 102 can perform predictions indirectly or directly. For example, an indirect prediction may include determining a target KPI's (e.g., emissions) baseline and computing an efficiency baseline as throughput divided by the KPI's baseline. Alternatively, a direct prediction may directly baseline efficiency based on one or more machine learning models learning what efficiency should be under different scenarios and operating conditions. It will be appreciated that there may be many different types of derived resources (e.g., of which emissions may be one) that can be baselined either directly or indirectly.

In some embodiments, asset inputs and/or asset outputs can include resources. In some embodiments, the machine learning-based resource prediction and optimization system 102 may use and obtain data from many different data sources 108 and types of data sources. For example, the machine learning-based resource prediction and optimization system 102 may obtain data indicating the gas composition of a mixture (e.g., broken down by component). The mixture may be a resource input to various assets, such as generators. The data may also indicate fluid flow, asset outputs (e.g., generator outputs), other asset inputs, the heat of combustion, operating conditions, ambient conditions (e.g., temperature, humidity, precipitation, solar irradiance, wind speed, etc.), production schedules, utility usage, maintenance events, work orders, satellite imagery, and/or other sensor data (e.g., from third-party sensors).

Some or all of this data may comprise sensor data, and the sensors may comprise assets or be associated with assets (e.g., associated with particular equipment). Operating conditions can include, for example, discharge pressure and temperature, suction pressure and temperature, compressor power, schedule of facility-level or equipment-level maintenance activities or shutdowns, number of types of assets, the states of various assets, and the like. Any subset of this data can be used as model input features (or, simply, model inputs) directly or as input to more sophisticated feature engineering methods.

The machine learning-based resource prediction and optimization system 102 can provide artificial intelligence-driven baseline predictions. For example, the machine learning-based resource prediction and optimization system 102 can predict how particular assets should operate (e.g., the amount of emissions they should produce) given various conditions (e.g., current conditions, historical conditions, simulated conditions). The conditions may include some or all of the data (including operating conditions) discussed above. In some implementations, machine learning-based resource prediction and optimization systems can generate estimates of what resources could have been in the past, given current conditions. Baseline predictions can be configured to predict different baselines, such as an average-case resource baseline, best-case resource baseline, and/or worst-case resource baseline across time and/or space (cohort of peer assets). The data may be represented by time series data over various periods.

The communications network 112 may represent one or more computer networks (e.g., LAN, WAN, or the like) or other transmission mediums. The communication network 112 may provide communication between the systems, engines, datastores, distributed compute and storage resources, and/or the like, described herein. In some embodiments, the communication network 112 includes one or more computing devices, routers, cables, buses, and/or other network topologies (e.g., mesh, and the like). In some embodiments, the communication network 112 may be wired and/or wireless. In various embodiments, the communication network 112 may include the Internet, one or more wide area networks (WANs) or local area networks (LANs), one or more networks that may be public, private, IP-based, non-IP based, and so forth.

FIG. 2 depicts a diagram 200 of an example system flow of a machine learning-based resource prediction and optimization system 102 using hierarchical aggregation to generate accurate results at the facility level when there are different types of assets and/or dependencies according to some embodiments.

In some implementations, the machine learning-based resource prediction and optimization system 102 performs hierarchical aggregation of AI-based forecasts and baselines. This can be particularly useful when calculating/predicting resource outputs/inputs at a facility level. More specifically, the accurate results cannot typically be obtained by merely summing the resource outputs/inputs unless the facility only has one type of asset and there are no dependencies between assets. For example, if there are different assets with dependencies with each other, summing the resource inputs/outputs would produce inaccurate results because some inputs/outputs may be “double-counted.” Accordingly, the machine learning-based resource prediction and optimization system 102 may use flexible hierarchical aggregation to produce accurate results at the facility level when there are different types and levels of assets and/or dependencies. An asset's baseline can be defined as an aggregation of its child assets' baselines. In this case, the union of features across child assets' baseline models can be included in feature contribution explanations of AI baselines, and contribution values can be aggregated via weighting by child asset's target KPI magnitude over a selected period. For example, a customer may want to baseline performance for a front-end process area which does not have any sensors directly associated with it and thus define the process area baseline as the sum of baselines across disjoint child machines located within the area.

In some implementations, artificial intelligence models of the machine learning-based resource prediction and optimization system 102 can be applied directly at any level of the customer's hierarchy (e.g., facility, business unit, system, subsystem, sensor, etc.). Model outputs can be aggregated up to higher levels (e.g., plant, business unit, and enterprise levels). This approach can require fewer models (e.g., no need for models at the higher levels of aggregation). If a probabilistic approach is used, then the aggregation can leverage the properties of the underlying distributions. Some distributions (e.g., Gaussian) can be summed up from a lower level (e.g., sensor) to a higher level (e.g., facility) while still having the same distribution type, but the parameters will change. The aggregation pays attention to whether the lower-lever distributions are independent or not. For example, if two assets (e.g., two pieces of equipment) have correlated resource outputs (e.g., greenhouse gas emissions), then their correlation can be factored in when estimating the aggregated resource output for the facility.

In one example, the aggregation can involve model parameter estimation at the asset level (e.g., equipment level), parameter aggregation to the facility level and time-invariant mapping of quantiles (e.g., expressed as a function of the model parameters) applied separately at the asset (e.g., equipment) and facility levels (e.g., as shown in FIG. 2 ). The aggregation from sensors to facility can be reframed as a disaggregation from facility to sensor in terms of end-user workflows.

In the example of FIG. 2 , the machine learning-based resource prediction and optimization system 102 obtains training data specific to different assets (e.g., equipment) of a facility in steps 202 - 1 to 202 -N. In steps 204 - 1 to 204 -N, the machine learning-based resource prediction and optimization system 102 estimates model parameters from the training data. In step 206 - 1 to 206 -N, the machine learning-based resource prediction and optimization system 102 determines time invariant mappings of quantiles for each of the equipment and applies that to live (e.g., real-time) data to predict baseline resource predictions for each equipment in steps 208 - 1 to 208 -N. In step 210 , the machine learning-based resource prediction and optimization system 102 aggregates the model parameters accounting for equipment correlation to estimate the facility model parameters from the training data in step 212 . In step 214 , the machine learning-based resource prediction and optimization system 102 determines the time-invariant mapping of quantiles and applies to live data to determine baseline predictions for the entire facility in step 216 .

Quantiles may be selected by a variety of different methods, such as probabilistic methods and quantile regression methods. In some embodiments, the machine learning-based resource prediction and optimization system 102 can build a Bayesian probabilistic GAM model, since this avoids quantile crossover and allows the system 102 to aggregate predictions from equipment level to facility level. Pygam implementation can also be used to allow the system 102 to construct a B-spline GAM which predicts mean and variance (this variance may be dependent on features).

FIG. 3 depicts a diagram 300 of an example of a machine learning-based resource prediction and optimization system 102 according to some embodiments. In the example of FIG. 3 , the machine learning-based resource prediction and optimization system 102 includes a management module 302 , a sensor detection module 304 , a resource baseline prediction module 306 , a cohort baseline prediction module 308 , a forecasting module 310 , a degradation detection module 312 , an optimization module and recommendation module 314 , an artificial intelligence traceability module 316 , a model management module 318 , a machine learning model training module 320 , an orchestrator module 322 , a masking module 324 , a hierarchical aggregation module 326 , a model deployment module 328 , a model input module 330 , an interface module 332 , a setpoint optimization module 334 , a production schedule optimization module 336 , an alerting module 338 , a communication module 340 , and a machine learning-based resource prediction and optimization system datastore 350 .

The management module 302 can function to manage (e.g., create, read, update, delete, or otherwise access) data associated with the machine learning-based resource prediction and optimization system 102 . The management module 302 can manage some or all of the of the datastores described herein (e.g., machine learning-based resource prediction and optimization system datastore 350 , data sources 108 ) and/or in one or more other local and/or remote datastores. It will be appreciated that datastores can be a single or multiple datastore local to the machine learning-based resource prediction and optimization system 102 and/or single or multiple datastores remote from the machine learning-based resource prediction and optimization system 102 . The datastores described herein can comprise one or more local and/or remote datastores. The management module 302 can perform operations manually (e.g., by a user interacting with a GUI) and/or automatically (e.g., triggered by one or more of the modules 304 - 340 ). Like other modules described herein, some or all the functionality of the management module 302 can be included in and/or cooperate with one or more other modules, services, systems, and/or datastores.

In some embodiments, the management module 302 can manage, integrate, and/or normalize disparate data from disparate data sources. For example, the management module 302 can integrate asset data, utility data, production schedule data, facility, third-party data (e.g., weather service data), and the like. The data can include historical data, real-time or live data, time series data, etc. The management module 302 may use predefined integration rules to integrate some or all of the data described herein.

The sensor detection module 304 can function to detect actual (e.g., current, real-time, live, etc.) sensor values. The sensor values may be detected directly from the assets, facilities, and/or third-party services and assets (e.g., by monitoring sensors of the assets, factories, and/or third-party services and assets. The sensor detection module 304 may detect time series data and include timestamps and/or metadata in the time series data.

The resource baseline prediction module 306 can function to resource baseline values for organizations (e.g., groups of facilities and/or assets). The resource baseline prediction module 306 can predict expected resource baseline values and optimal (or, ideal) resource baseline values. The predicted resource baseline values may correspond to expected resource consumption and optimal resource consumption of the asset. The predicted resource baseline values may correspond to expected resource production (or, output) and optimal resource production. As used herein, “optimal” may be determined based on one or more rules and/or threshold. For example, an operating manual or specification of an asset may indicate the optimal values for that asset. The optimal values may also be determined using machine learning. For example, machine learning models may be used to take model outputs associated with various assets over a period time and determine that the top 5% (e.g., top 5% more efficient) of those assets represent the optimal values for that that asset based upon its current vector of operating conditions.

In some implementations, the resource baseline prediction module 306 can make predictions using one or more machine learning models. Outputs of the machine learning models can be applied to various resources of interest, including any of the resources described herein. Machine learning inputs (e.g., features), can be used by the models to learn the baselines. The features may be directly based on data described herein, and/or transformations of that data.

It will be appreciated that the exact implementation of baseline predictions may vary and may depend on how many models are used and how they are trained. Additionally, baseline values can be estimated at any resolution (e.g., hourly, daily, monthly), and can be applied at the organization level (e.g., one or more facilities) or any finer-grained level (e.g., facility level and/or asset level).

Various models are available for baselining (e.g., resource and cohort baselining), and may be used by the resource baseline prediction module 306 , the cohort baseline prediction module 308 , the forecasting module 310 , the optimization and recommendation module 314 , and/or other modules described herein. The models may include linear regression models, tree-based models (e.g., GBMs, RF, Decision Tree) and GAMs (generalized additive model), and temporal fusion transformers (TFTs). In some examples GAMs may be preferred because they (1) can extrapolate, which tree-based models typically cannot do, (2) can model nonlinear dependencies, which linear models cannot do, and (3) offer comparable performance to GBMs.

In some implementations, once the model is deployed, the baseline predictions are expected to deviate from the actual measurements. However, resource baseline prediction module 306 needs to understand if this is due to the model failing due to data drift or if the equipment behavior is changing. In one example, the resource baseline prediction module 306 can select a reference period for the baseline. The goal of the baseline model is to compare current performance to historical performance. Over time, the dependencies between the target variable that is being baselined, and the features used to describe it change (e.g., this may be referred to as concept drift). The resource baseline prediction module 306 can model this change with an interaction effect between time and the features. There are multiple ways the resource baseline prediction module 306 can accomplish this task, such as (1) nonlinear surface interaction term between time and feature, (2) linear surface interaction term between time and feature, and (3) time as a term in the model. Each one of these options offers a tradeoff between overfitting and accuracy, and the last option can be used to model the concept drift. Once the resource baseline prediction module 306 has included this time term in the model, the resource baseline prediction module 306 can choose a time t that can be baselined against. This can allow, for example, the resource baseline prediction module 306 to specifically compare current performance to a given point in time.

The cohort baseline prediction module 308 can function to evaluate or grade facilities and/or assets in relation to a cohort, wherein the cohort can include similar facilities and/or assets. For example, the types of assets in a cohort can include equipment, machines, etc. In some implementations, the cohort can be homogenous in that all assets are equipment and equipment are not being compared with facilities. The comparisons in a cohort can provide additional insight into relative performance metrics (e.g., efficiency, emissions, emissions intensity, etc.). Cohorts can be specified either manually or automatically. Automatic cohort identification can leverage AI, such as clustering or nearest neighbors. Attributes of assets (such as age, type, throughput, location, operating characteristics, etc.) can be used in automatically determining cohorts. In some implementations, end users may specify which attributes are to be used in automatically creating cohorts. An example method of cohort baseline prediction is shown in FIG. 6 .

The forecasting module 310 can function to provide AI-driven forecasting. Forecasting can include predicting future resource inputs (e.g., fuel consumption), and future resource or byproduct outputs (e.g., greenhouse gas emissions). In the most common case, the forecasting module can leverage historical and future time series data (e.g., such as large tensors of asset telemetry data and/or historical a

CLAIMS

Claims ( 28 )

What is claimed is:

1 . A method comprising:

obtaining asset data associated with an asset, wherein the asset data includes time series data associated with a resource consumed by the asset or output by the asset to serve as a target variable to baseline; predicting, by a machine learning model based on the time series asset data, a target resource baseline value associated with the asset based upon the asset's operating conditions; detecting actual resource values associated with the asset based on one or more sensor values; and determining a recommendation based on a difference between the target resource baseline value and the actual resource values for the target variable.

2 . The method of claim 1 , wherein the target resource baseline value corresponds to any of an expected resource consumption of the asset and an optimal resource consumption of the asset.

3 . The method of claim 1 , wherein the target resource baseline value corresponds to any of an expected resource output of the asset and an optimal resource output of the asset.

4 . The method of claim 1 , wherein the actual resource values include real-time time series values associated with the resource consumed or output by the asset, and the target resource values comprise time series values.

5 . The method of claim 1 , wherein the resource comprises any of greenhouse gas emissions, electricity consumption or generation, combusted fuels, water, waste, and compressed air, derived resources, and other metrics informing operational or sustainability performance.

6 . The method of claim 1 , further comprising:

simulating an adjustment to the asset; predicting an outcome of the simulation using one or more additional machine learning models; and generating one or more instructions to adjust the asset based on a comparison of the predicted outcome and a threshold value.

7 . The method of claim 1 , further comprising detecting degradation of the asset based on the difference between the target resource baseline value and the detected actual resource values over a period.

8 . The method of claim 1 , further comprising:

predicting, based on one or more additional machine learning models and the detected actual resource values, a future resource value associated with the asset; determining, based on a comparison of the future resource value and the predicted target resource value, a corrective action; and causing an adjustment to the asset based on the corrective action.

9 . The method of claim 1 , further comprising:

determining, based on the difference between the predicted resource baseline value and the detected actual resource values, a potential resource reduction or increase associated with the asset and the resource.

10 . The method of claim 4 , further comprising:

tracking a reduction or increase of the detected resource consumption or output over a period of time against the potential resource reduction or increase associated with the asset and the resource; and determining one or more corrective actions based on the tracking.

11 . The method of claim 1 , wherein the predicted target resource baseline value comprises time series data values predicted over a period of time.

12 . The method of claim 1 , further comprising:

masking a portion of the asset data, wherein the target resource baseline value is predicted based on the asset data excluding the masked portion of the asset data.

13 . The method of claim 1 , wherein the asset comprises an asset of multiple assets, and further comprising:

obtaining other asset data associated with the multiple assets, wherein the other asset data includes other time series data associated with a resource consumed by the other assets of the multiple assets or output by the other assets to serve as other target variables to baseline; predicting other target resource baseline values for each of the other assets of the multiple based upon the other asset's operating conditions; detecting other actual resource values associated with the other assets based on one or more other sensor values; and determining another recommendation based on differences between the other target resource baseline values of the other assets and the other actual resource values.

14 . The method of claim 1 , further comprising:

providing the recommendation to the asset, thereby causing an adjustment to the asset.

15 . A method comprising:

determining a cohort of assets; obtaining asset data associated with a particular asset of the cohort of assets, wherein the asset data includes time series data associated with a resource consumed by the asset or output by the asset; predicting, for each asset of the cohort of assets, a respective target resource baseline value, wherein each of the respective target resource baseline values is predicted by a different machine learning model based on the asset data associated with the particular asset of the cohort of assets; aggregating the respective predicted target resource baseline values; and determining a difference between the target resource baseline value of the particular asset and the aggregated respective predicted target resource baseline values.

16 . The method of claim 15 , wherein the aggregated respective predicted target resource baseline values comprise a minimum value or a maximum value of the respective target resource baseline values over a period of time.

17 . The method of claim 15 , wherein each of the different machine learning models comprises a respective machine learning model trained for a different asset of the cohort of assets.

18 . The method of claim 15 , wherein the respective predicted target resource baseline values correspond to any of an expected resource consumption or expected resource output of the assets of the cohort of assets.

19 . The method of claim 15 , further comprising performing one or more corrective actions based on the difference between the target resource baseline value of the particular asset and the aggregated respective predicted target resource baseline values.

20 . The method of claim 15 , wherein the asset data includes real-time time series data associated with the resource consumed or output by the cohort of assets.

21 . The method of claim 15 , wherein the cohort of assets is determined based one or more similar attributes of the assets.

22 . A method comprising:

identifying asset data associated with an asset based on a query, wherein the asset data includes time series data associated with a resource consumed by the asset or output by the asset; predicting, by a machine learning model based on the time series asset data, a target resource baseline value associated with the asset; detecting actual resource values associated with the asset based on one or more sensor values; determining a difference between the target resource baseline value and the detected actual resource values; and generating a recommendation, by the machine learning models based on the difference, a natural language recommendation associated with the asset.

23 . The method of claim 22 , further comprising,

wherein the recommendation is generated as a natural language response to the query by one or more omni-modal models.

24 . The method of claim 22 , wherein the machine learning model is trained on production data associated with the asset, and wherein the production data is structured by a model driven architecture.

25 . A method comprising:

obtaining facility data associated with a facility, wherein the facility data includes time series data associated with a resource consumed by the facility or output by the facility to serve as a target variable to baseline; predicting, by a machine learning model based on the time series facility data, a target resource baseline value associated with the facility based upon the facility's operating conditions; detecting actual resource values associated with the facility based on one or more sensor values; and determining a recommendation based on a difference between the target resource baseline value and the actual resource values for the target variable.

26 . The method of claim 25 , wherein the machine learning model is trained on the facility data, and wherein the production data is structured by a model driven architecture.

27 . A method comprising:

receiving user specifications associated with one or more assets; generating, based on the specifications, one or more masking metrics and feature sets for multi-asset machine learning training and inference; training multiple machine learning models for multiple assets based on the masking metrics and the feature sets; selecting a respective trained machine learning model and a respective set of hyperparameters for each asset of the multiple assets; deploying each of the respective trained machine learning models; generating, by the deployed machine learning models, one or more machine learning outputs; and generating one or more recommendations based on the one or more machine learning outputs.

28 . The method of claim 27 , further comprising triggering one or more adjustments to at least one of the assets of the multiple assets based on the one or more recommendations.

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