Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

Abstract: A method of machine learning may include receiving an API documentation including an endpoint and corresponding metadata. The method may include receiving a verified API specification including a verified endpoint. The method may include comparing the endpoint to the verified endpoint. The method may include classifying the metadata. The metadata may be classified as a positive item responsive to the endpoint being substantially the same as the verified endpoint. The metadata may be classified as a negative item responsive to the endpoint not being substantially the same as the verified endpoint. The method may include generating a training dataset, which may include the metadata classified as a positive item or a negative item. The method may include generating a metadata model using a machine learning algorithm and the training dataset. The metadata model may be configured to be used to determine whether an unknown endpoint is a positive endpoint.

Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.