Short-term electricity price forecasting with recurrent regimes and structural breaks

Abstract

This paper develops a new approach to short-term electricity forecasting by focussing upon the dynamic specification of an appropriate calibration dataset prior to model specification. It challenges the conventional forecasting principles that establish that adaptive methods should place most emphasis upon recent data and that regime switching should likewise model transitions from the latest regime. The approach recognises that the most relevant dataset in the episodic, recurrent nature of electricity dynamics may not be the most recent. It applies cluster analysis to fundamental market regime indicators as well as structural time series breakpoint analyses. Forecasting is based upon applying a hybrid fundamental optimisation model with a neural network, to the appropriate calibration data. The results outperform other benchmark models in backtesting on the Iberian electricity market of 2017, which presents a considerable number of market structural breaks and evolving market price drivers.