Smart meter data is being used to improve the accuracy of intraday load forecasting by considering customer behavior similarities. Unlike traditional load forecasting, smart meter data from advanced metering infrastructure provides a new perspective ranging from very short-term to long-term forecasting at various levels. This paper aims to enhance system level intraday load forecasting by applying clustering to identify customer groups with similar load patterns from smart meters before performing the forecasts.