Auditory-circuit-motivated deep network with application to short-term electricity price forecasting

Reliable electricity price forecasts are of great importance to operators and participants in power markets. However, due to mixing effects of various factors, electricity price fluctuations are too complex to extract hidden features for accurately modelling. Additionally, biologically-inspired ideas are promising in significantly improving the performance and rationality of deep forecasting networks. Based on the fact that the auditory system effectively handles complex sound signals with amplitude, frequency and wavelength characteristics, this paper explores an auditory-circuit-motivated deep network with three following modules for forecasting short-term electricity prices. Specifically, the coding module imitates that left and right ears receive the sound and convert it into electric signals in parallel, and codes the input electricity prices into multiple valuable features, improving the feature integrity and model stability. The analysis module imitates that the left and right hemispheres integrate and handle electric signals to produce higher auditory information, and captures nonlinear and short-term dependencies via stacking convolutional and gating operations. The forecasting module imitates that the high-level brain region pays attention to the external environment based on higher auditory information, and generates final forecasts via the attention mechanism. Based on the cooperation of the above three modules, the proposed deep network mimics the flowchart, structures and functions, thereby inheriting superior handling capability of the auditory circuit. Experiment results under two real-world sets show that the proposed deep network is superior to 13 baselines, and improves the mean absolute error by averages of 14.7 % and 20.6 % in normal parts, 12.9 % and 23.4 % in high-fluctuation parts, and 16.1 % and 16.2 % in peak-low parts, which is an effective supplementary model for electricity price forecasting.