In this paper we present an investigation for the short term (up 24 hours) load forecasting of the demand for the South Sulewesi's (Sulewesi Island -Indonesia) Power System, using a Multiple Linear Regression (MLR) method. After a brief analytical discussion of the technique, the usage of polynomial terms and the steps to compose the MLR model will be explained. Report on implementation of MLR algorithm using commercially available tool such as Microsoft EXCEL TM will also be discussed. As a case study, historical data consisting of hourly load demand and temperatures of South Sulawesi electrical system will be used, to forecast the short term load. The results will be presented and analysed potential for improvement using alternative methods is also discussed.

As accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional loads forecasting methods have been developed. In this paper we present the development of short term load forecaster using artificial neural network (ANN) models. Three approaches have been undertaken to forecast the load demand up to 24 hours ahead. The first model is a model that has 24 output nodes to forecast a sequence of 24 hourly loads at a time. The second ANN model forecasts the peak and valley load and the result is used to forecast the load profile, and finally a system with 24 separate ANNs in parallel, one for each hour of the days is used to forecast the load demand. These models are applied to the South Sulawesi Electricity System and the comparative summary of their performances are evaluated through simulation.