Tryggvi Jónsson

Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time-varying regression model. In a second step, time-series models, i.e., ARMA and Holt-Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pools Elspot, during a two year period covering 2010-2011. These results clearly demonstrate the practical benefits of accounting for the complex influence of these explanatory variables.

A Kalman filter based DSP method for prediction of seasonal financial time series with application to energy spot price prediction

In this work, energy spot price prediction is used to motivate a holistic signal processing approach to modeling and predicting nonstationary time series having a structure that is a mixture of quasi-periodic, cyclo-stationary, and locally regular stochastic components. The approach is iterative in the sense that the Kalman filter model used for estimation and prediction is repeatedly adjusted, based on exposure of hidden model structure identified using point spectrum and cyclo-stationary signal processing tools. It is shown that this holistic approach achieves reasonable 1-day and 7-day spot price prediction accuracy.

Statistical analysis of the impact of wind power on market quantities and power flows

In view of the increasing penetration of wind power in a number of power systems and markets worldwide, we discuss some of the impacts that wind energy may have on market quantities and cross-border power flows. These impacts are uncovered through statistical analyses of actual market and flow data in Europe. Due to the dimensionality and nonlinearity of these effects, the necessary concepts of dimension reduction using Principal Component Analysis (PCA), as well as nonlinear regression are described. Example application results are given for European cross-border flows, as well as for the impact of load and wind power forecasts on Danish and German electricity markets.