DaNSe: A Dilated Causal Convolutional Network Based Model for Load Forecasting

Abstract

Integration of renewable sources into energy grids has reduced carbon emission, but their intermittent nature is of major concern to the utilities. In order to provide an uninterrupted energy supply, a prior idea about the total possible electricity consumption of the consumers is a necessity. In this paper, we have introduced a deep learning based load forecasting model designed using dilated causal convolutional layers. The model can efficiently capture trends and multi-seasonality from historic load data. Proposed model gives encouraging results when tested on synthetic and real life time series datasets.