Nonlinear Excess Demand Model for Electricity Price Prediction

Variety of models and estimation techniques have been proposed for electricity price forecasting in the literature. We contribute by introducing a dynamic forecasting model for hourly electricity prices based on nonlinear excess demand specification. Our modelling framework depends on the neoclassical price adjustment equation that necessitates prices adjust toward equilibrium at a rate that is proportional to the excess demand. We approximate the adjustment as a nonlinear (cubic) function of the excess demand which itself is modeled as a latent factor. We show that nonlinear relation of excess demand and price leads to a more accurate description of price evolution toward equilibrium and with this framework, the equilibrium forecast for the price is given by a nonlinear equation of the excess demand that can be modeled as a function of important variables of supply and demand. This generates an advantage to forecasters in employing all the information on supply and demand functions in price pre-diction, rather than simply modelling the price on an ad-hoc manner. We further develop a maximum likelihood estimator with excess demand defined as a normally distributed random variable conditional on observables. We demonstrate our likelihood estimator by using data from Turkish electricity market. Our modelling framework implies time varying volatility for prices which along with the nonlinear mean function, brings two important features of time series modelling dynamics together in parsimonious model.