Medium-term electric load forecasting using singular value decomposition

Medium-term load forecasting is an important stage in electric power system planning and operation. It is used in maintenance scheduling, and to plan for outages and major works in the power system. A new technique is proposed which uses hourly loads of successive years to predict hourly loads and peak load for the next selected time span. The proposed method implements a new combination of some existing and well established techniques. This is done by first filtering out the load trend, then applying the SVD (singular value decomposition) technique to de-noise the resulting signal. Hourly load is thus divided to three main components: a) a load trend-following component, b) a random component, and c) a de-noised component. Results of applying the technique to the Jordanian power system showed that good forecasting accuracies are attained. In addition, the proposed method outperforms the traditional exponential curve fitting method. The peak load error was found to be less than 5% using the proposed methodology. It was also found that a lag period of 4 years suits the load forecasting purposes of the Jordanian power system. The proposed method is generic and can be implemented to the hourly loads of any power system.

► Hourly load of the year was decomposed into: trend, de-noised and random components, using polynomial regression and singular value decomposition.
► Medium term load forecast was done based on using the load components using a specified time span.
► A case study implemented the method using the hourly loads of the Jordanian power system for the years 1994-2008.
► Performance was acceptable when implemented to the hourly loads as well as to the yearly peak loads.
► The proposed methodology is generic and can be applied to forecasting any commodity or service.