A Dilated Convolutional Based Model for Time Series Forecasting

Smart grids infrastructure is rapidly adopting the recent technology to optimize the power generation and energy saving. The load forecasting in smart grids has been one such technology integration and accurate load forecasting models has been a challenge. With the advent of advanced infrastructure, huge data is being generated at different time frequencies, that can be used to build accurate load forecasting models. Focusing on the state-of-the-art machine learning techniques, in this work, we propose a load forecasting model of stacked dilated convolutional layers. The dilations efficiently captures the local trend and seasonality from the time series for future predictions. Proposed model has been trained on multiple time series data with varying frequencies. Results show that the proposed model is an improvement to the existing state-of-the-art.