QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting

Abstract

Real-time electricity market data is highly volatile and very noisy. The properties of such data make forecasting models difficult to develop, with traditional statistical models in particular affected by the “curse of dimensionality” for such data. However, autoencoders, or neural networks specifically designed to reduce the noise and dimensions of input data, may prove useful to advance the accuracy of real-time price forecasting models. This paper studies the optimal design of such an autoencoder, developing a quadruple branch, CNN-based autoencoder (QCAE) which is pre-trained and then directly linked to a forecasting model. The QCAE compresses the input data in both time and feature directions. Ablation analyses verify the architecture of the QCAE, and its integration with the forecasting model is tested and validated on fifty generators in the New York Independent System Operator (NYISO) power grid. The QCAE forecasting framework outperforms benchmark and state-of-the-art models with an average improvement of 6.3% in sMAPE and 3.10% in MAE.