Volume 52

Literature Review for Large-Scale Aggregate Load Forecasting Using Large Volumes of Smart Meter Data Quoc Viet Nguyen, Sergio Potenciano Menci, Joaquin Delgado Fernandez

Abstract

Large-scale short-term aggregate load forecasting involves predicting energy consumption across geographic areas or large sets of users. This practice is crucial in power systems, particularly for energy suppliers. The widespread installation of smart metering technology has facilitated the collection of extensive data on user load profiles. By incorporating such granular data, large-scale load forecasting becomes more accurate and reliable, capturing the variability and trends across consumer segments. However, transforming smart-meter data into effective load forecasting models faces significant challenges. For instance, smart-meter data presents issues related to its high volume, variety, and fine-grained temporal resolution. Consequently, different techniques can be considered to mitigate these issues before applying forecasting models to the data. This paper conducts a two-step literature review to provide insight into the data, forecasting approaches, and model evaluation used in large-scale, short-term aggregate load forecasting from smart-meter data. We propose classifying the different forecasting approaches into integrated, residential-based, and cluster-based strategies. We additionally draw insight into the effect of data volume on forecasting models, performance comparison between forecasting approaches, and the tendency of model complexity in this research domain.

Keywords Load forecasting, big data, artificial intelligence, large-scale, large volume, energy supplier, smart grid