Forecasting system using machine learning and ensemble methods
OwnerRoger N. Anderson, Albert Boulanger +4inventors
InventorsRoger N. Anderson, Albert Boulanger, Leon L. Wu, Viabhav Bhandari, Somnath Sarkar, Ashish Gagneja
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Abstract
Techniques for determining forecast information for a resource using learning algorithms are disclosed. The techniques can include an ensemble of machine learning algorithms. The techniques can also use latent states to generate training data. The techniques can identify actions for managing the resource based on the forecast information. The resource can include energy usage in buildings, distribution facilities, and resources such as Electric Delivery Vehicles. The resource can also include forecasting package volume for businesses.
Claims30
A method for determining forecast information for a resource, the method comprising:
- receiving data related to the resource;
- defining one or more predictor variables from at least a portion of the data;
- generating the forecast information, including:
- generating one or more optimized learning model parameters based at least in part on the one or more predictor variables;
- using one or more forecasting models to generate the forecast information based at least in part on the one or more optimized learning model parameters;
- and
- identifying one or more actions based at least in part on the forecast information.
A system for determining forecast information for a resource, the system comprising:
- a database to store data related to the resource,
- a memory, coupled to the database, and at least one processor that accesses the memory, the memory having instructions which when executed, perform a method comprising:
- receiving the data related to the resource;
- defining one or more predictor variables from at least a portion of the data;
- generating the forecast information, including:
- generating one or more optimized learning model parameters based at least in part on the one or more predictor variables;
- using one or more forecasting models to generate the forecast information based at least in part on the one or more optimized learning model parameters;
- and
- identifying one or more actions based at least in part on the forecast information;
- and
- a dynamic scheduler, coupled to the memory, configured to receive the forecast information and the one or more actions.
A method for determining forecast information for a resource, comprising:
- receiving data related to the resource;
- generating the forecast information using an error weighted ensemble method, including
- determining the forecast information based at least in part on one or more forecasting models and the data;
- and
- identifying one or more actions based at least in part on the forecast information.
Dependencies are inferred from the claim text.