Predictive modeling of energy consumption in a cellular network
US12192806B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 22, 2021 |
| Grant date | Jan 7, 2025 |
| Priority date | — |
| Expiry date | Apr 19, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W84/042
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.