Abstract
The short term load forecasting plays a crucial role in optimal operation and scheduling of the generation resources in power system. In this work, Auto-Regressive Integrated Moving Average (ARIMA), Multiple Linear Regression (MLR), Recursive Partitioning and Regression Trees (RPART), Conditional Inference Trees (CTREE) with Bootstrap Aggregating (BAGGING), and Random Forest (RF) models have been tested and compared for short term load forecasting. These methods have been tested on a sample electricity load data of a residential area containing data sets for training and testing.
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