61: Using RNN to analyze electricity residential load curves and perform short-term prediction

Music City Center 

The widespread deployment of smart meters in the residential and tertiary sectors has made it possible to collect high-frequency electricity consumption data at the consumer level (individuals, professionals, etc.). This data is a raw material for research on the prediction of electricity consumption at this level. The majority of this research is largely aimed at meeting the needs of industry, such as applications in the context of smart homes and programs for managing and reducing consumption.

The objective of this work is to deploy or implement short-term (D + 1) electrical load forecasting models at the consumer level. The complexity of the subject lies in the fact that consumption data on this scale is very volatile. Indeed, it includes a large amount of noise and depends on the consumer's lifestyle and consumption habits.

In this study, RNN-LSTM models were deployed to predict household load, taking into account various industrial constraints.

The models were tested and evaluated on a large sample of disparate load curves in the residential sector. An approach was also proposed for the prediction of the most volatile load curves.

Machine Learning

Deep Learning

RNN-LSTM

Electricity load curves

Short-term individual prediction 

Section on Statistical Learning and Data Science