Singular Spectrum Analysis for Forecasting of Electric Load Demand

Abstract

This paper presents the technique of Singular Spectrum Analysis (SSA) and its application for electric load forecasting purposes. SSA is a relatively new non-parametric data-driven technique developed to model non-linear and/or non-stationary, noisy time series. SSA is able to decompose the original time series into the sum of independent components, which represent the trend, oscillatory behavior (periodic or quasi- periodic components) and noise. One of the main advantages of SSA compared to other non-parametric approaches is that only two parameters are required to model the time series under analysis. An example of application is given, with regards to forecasting the monthly electric load demand in a Venezuelan region served by wind power generators. In this case, careful demand estimation is required since the wind generation output could be highly variable and additional conventional generation or transmission links would be required to satisfy the load demand. A comparison with other classical time-series approaches (like exponential smoothing and Auto Regressive Integrated Moving Average models) is presented. The results show that SSA is a powerful approach to model time series, capable of identifying sub-time series (trends along with seasonal periodic components).