An Adaptive Functional Autoregressive Forecasting Model to Predict Electricity Price Curves

Ying Chen (National University Singapore)
Wednesday, June 24, 2015 - 10:00am
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

Electricity price forecasting is becoming increasingly relevant in the competitive energy markets. We provide an approach to predict the whole electricity price curves based on the adaptive functional autoregressive (AFAR) methodology. The AFAR has time varying operators that allow it to be safely used in both stationary and non-stationary situations. Under stationarity, we develop a consistent maximum likelihood (ML) estimator with closed form, where the likelihood function is defined on the parameters' subspace or Sieves. For non-stationary data, the estimation is conducted over an interval of local homogeneity, over which the time varying data generating process can be approximated by an FAR model with constant operators. The local interval is identified in a sequential testing procedure. Simulation study illustrates good finite sample properties of the proposed AFAR modeling. Real data application on forecasting California electricity daily price curves demonstrates a superior accuracy of the proposed AFAR modeling compared to several alternatives.