Machine learning-based resource prediction and optimization

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.

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.

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.

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.