# US 2026/0093221 A1 · Method and apparatus for energy management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0093221 A1 - Document ID: US20260093221A1 - Status: Published application - Kind: A1 - Application number: 19/329,212 - Filed: 2025-09-15 - Published: 2026-04-02 - Earliest claimed: 2024-09-30 - Assignees: Nokia Solutions and Networks Oy - Applicants: Nokia Solutions and Networks Oy - Inventors: Buvaneswari RAMANAN; Manzoor KHAN; Aditya GUDAL; Juergen SIENEL; Senthil Muthu Kumar A C - Web page: https://patentgrep.com/patent/US20260093221A1 ## CPC - G05B 19/042: using digital processors (G05B19/05 takes precedence) - G06Q 50/06: Energy or water supply - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand} - H04L 9/50: {using hash chains, e.g. blockchains or hash trees} - G05B 2219/2639: Energy management, use maximum of cheap power, keep peak load low - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Examples of the disclosure relate to 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. ## Claims ### Claim 1 An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: 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. ### Claim 2 (depends on 1) The apparatus according to claim 1, wherein the first input and the first output of the apparatus are communicatively connected to a blockchain. ### Claim 3 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: obtaining a federated trained embedding generation model; determining the embedding based on the feature vector using the federated trained embedding generation model. ### Claim 4 (depends on 3) The apparatus according to claim 3, wherein the apparatus is caused to further perform: receiving information related to an embedding generation model from a second input of the apparatus; determining a second set of gradients by training the embedding generation model using the feature vector; providing the second set of gradients to a third output of the apparatus; receiving the federated trained embedding generation model from the second input of the apparatus. ### Claim 5 (depends on 4) The apparatus according to claim 4, wherein, the second input and the third output of the apparatus are communicatively connected to the blockchain or a further blockchain. ### Claim 6 (depends on 1) The apparatus according to claim 1, wherein the information related to the machine learning model or the information related to the embedding generation model comprises: an architecture of the respective model; initial states of the respective model; and hyperparameters of a training process related to the respective model. ### Claim 7 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: storing the data from smart energy sensor; determining at least one of statistics of historic consumption; statistics of historic energy production, and statistics of historic energy storage based on the stored data; determining the feature vector based on the statistics. ### Claim 8 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: obtaining at least one of: characteristics of the energy generating device; characteristics of the energy consuming entities; data from an energy metering device within the household; information about the household and/or building of the household; information about environment; and temporal information. ### Claim 9 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: determining the feature vector further based on at least one of: characteristics of the energy generating device; characteristics of the energy consuming entities; data from an energy metering device within the household; information about the household and/or building of the household; information about environment; and temporal information. ### Claim 10 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: receiving an indication of the cluster to which the apparatus is assigned from the first input of the apparatus. ### Claim 11 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: sending a message to the first output or the second output of the apparatus to indicate a deviation of the embedding generation model or the machine learning model is higher than a predetermined threshold. ### Claim 12 (depends on 1) The apparatus according to claim 1, wherein the apparatus is caused to further perform: encrypting at least one of: the embedding, the first set of gradients, and the second set of gradients, before providing it to the respective output of the apparatus. ### Claim 13 (depends on 12) The apparatus according to claim 12, wherein the encrypting is performed using Homomorphic Encryption, HE, or Secure Multiparty computation, SMPC. ### Claim 14 (depends on 1) The apparatus according to claim 1, wherein the apparatus is suitable for use by an energy gateway. ### Claim 15 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. ### Claim 16 (depends on 15) The method according to claim 15, wherein the first input and the first output of the apparatus are communicatively connected to a blockchain. ### Claim 17 (depends on 15) The method according to claim 15, further comprising: obtaining a federated trained embedding generation model; determining the embedding based on the feature vector using the federated trained embedding generation model. ### Claim 18 (depends on 17) The method according to claim 17, further comprising: receiving information related to an embedding generation model from a second input of the apparatus; determining a second set of gradients by training the embedding generation model using the feature vector; providing the second set of gradients to a third output of the apparatus; receiving the federated trained embedding generation model from the second input of the apparatus. ### Claim 19 (depends on 18) The method according to claim 18, wherein, the second input and the third output of the apparatus are communicatively connected to the blockchain or a further blockchain. ### Claim 20 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.