Day types identification of Algerian electricity load using an image based two-stage approach

Abstract

Short term electricity load forecasting is one of the main concerns for electricity producers in regular system planning, where electricity demand is influenced by the day type among other factors that must be identified before modeling to ensure good load balance. This paper proposes a two-stage approach for identifying day types based on an image of the daily load curve. In the first stage, a set of day classes of load profiles using K-Means clustering algorithm is created, while in the second stage, the Time-Series Visualization method is used to build a classification model able to assign different days to the existing classes, detecting visual characteristics from daily load data curves. This classification model could be used in the forecasting process either by including the day-type as an input or by modeling each day-type independently.