Machine learning with parallel neural networks for analyzing and forecasting electricity demand

Traditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods.