A CNN-Sequence-to-Sequence network with attention for residential short-term load forecasting

Mosbah Aouad, Hazem Hajj, Khaled Shaban, Rabih A. Jabr, Wassim El-Hajj

Abstract:

Residential short-term load forecasting has become an essential process to develop successful demand response
strategies, and help utilities and customers optimize energy production and consumption. Most previous works
focused on capturing the spatial and temporal characteristics of residential load data but fell short in accurately
comprehending its variations and dynamics. The challenges come from the high non-linearity and volatility of
the electric load data, and their complex spatial and temporal characteristics. To address these challenges, we
propose a hybrid deep learning approach consisting of a Convolutional Neural Network and an attention-based
Sequence-to-Sequence network. The model aims at capturing the spatial and temporal features from time-series
data, the irregular load pattern, and the frequent peak consumption values to improve the overall quality of
the forecasts. The proposed model is compared to several state-of-the-art approaches, and the performance is
validated on the residential load data for a household in Sceaux, France. The results showed an improvement
of 9.6% in the mean square error on different prediction time horizons. The proposed approach produced more
accurate real-time forecasts and showed better adaptation at peak consumption instances.