Method and apparatus for energy management

A method performed by an apparatus, comprising:

• obtaining data from a smart energy sensor connected to at least one of:
• an energy generating device, an energy consuming entity;
• and an energy storage device within a household;
• determining a feature vector based on the obtained data, wherein, the feature vector comprises information related to at least one of:
• energy generation, energy consumption, energy surplus at the household;
• determining an embedding based on the feature vector, wherein the embedding is suitable for a comparison across a plurality of households for clustering the households;
• providing the embedding to a first output of the apparatus;
• receiving, from a first input of the apparatus, information related to a machine learning model for a cluster to which the apparatus is assigned;
• wherein, the cluster is assigned based on the embedding;
• determining a first set of gradients by training the machine learning model based on the feature vector;
• providing the first set of gradients to the first output of the apparatus;
• receiving, from the first input of the apparatus, a trained machine learning model, wherein, the trained machine learning model is trained base on the first set of gradients;
• predicting, using the trained machine learning model, at least one of:
• the energy generation, energy consumption, and energy surplus;
• or disaggregating, using the trained machine learning model, an overall consumption to individual consumptions of a plurality of energy consuming entities within the household;
• providing a result of the predicting or the disaggregating to a second output of the apparatus.

A non-transitory computer readable medium comprising instructions, when executed by an apparatus, cause the apparatus to perform at least the following:

• obtaining data from a smart energy sensor connected to at least one of:
• an energy generating device, an energy consuming entity;
• and an energy storage device within a household;
• determining a feature vector based on the obtained data, wherein, the feature vector comprises information related to at least one of:
• energy generation, energy consumption, energy surplus at the household;
• determining an embedding based on the feature vector, wherein the embedding is suitable for a comparison across a plurality of households for clustering the households;
• providing the embedding to a first output of the apparatus;
• receiving, from a first input of the apparatus, information related to a machine learning model for a cluster to which the apparatus is assigned;
• wherein, the cluster is assigned based on the embedding;
• determining a first set of gradients by training the machine learning model based on the feature vector;
• providing the first set of gradients to the first output of the apparatus;
• receiving, from the first input of the apparatus, a trained machine learning model, wherein, the trained machine learning model is trained base on the first set of gradients;
• predicting, using the trained machine learning model, at least one of:
• the energy generation, energy consumption, and energy surplus;
• or disaggregating, using the trained machine learning model, an overall consumption to individual consumptions of a plurality of energy consuming entities within the household;
• providing a result of the predicting or the disaggregating to a second output of the apparatus.