Big data

Engineering

Biology

Identification (biology)

Support vector machine

Smart grid

Data mining

Computer science

Botany

Demand response

Physics

Database

Astronomy

Data science

Artificial intelligence

Deep learning

Smart meter

Electricity

Convolutional neural network

Machine learning

Data modeling

Metre

Electrical engineering

IEEE Transactions on Smart Grid

Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.

Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data