A data-driven approach using deep learning time series prediction for forecasting power system variables

Abstract

This study investigates the performance of ‘Group Method of Data Handling’ type neural network algorithm in short-term time series prediction of the renewable energy and grid-balancing variables, such as the Net Regulation Volume (NRV) and System Imbalance (SI). The proposed method is compared with a Multi-layer Perceptron (MLP) neural network which is known as a universal approximator. Empirical validation results show that the GMDH performance is more accurate in compression with the most recent forecast which is provided by ELIA (Belgian transmission system operator). This study aims to practice the applicability of the polynomial GMDH-type neural network algorithm in time series prediction under a wide range of complexity and uncertainty related to the environment and electricity market