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.

1. USING SMART METER DATA TO IMPROVE THE ACCURACY OF INTRADAY
LOAD FORECASTING CONSIDERING CUSTOMER BEHAVIOR SIMILARITIES
ABSTRACT
With the deployment of advanced metering infrastructure (AMI), an avalanche of new
energy-use information became available. Better understanding of the actual power consumption
patterns of customers is critical for improving load forecasting and efficient deployment of smart
grid technologies to enhance operation, energy management, and planning of electric power
systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a
fresh perspective to the way load forecasting is performed, ranging from very short-term load
forecasting to long-term load forecasting at the system level, regional level, feeder level, or even
down to the consumer level. This paper addresses the efforts involved in improving the system
level intraday load forecasting by applying clustering to identify groups of customers with
similar load consumption patterns from smart meters prior to performing load forecasting.