Predicting Energy Demands of Retail Stores—Can Deep Learning Help?

In the context of the energy and climate crises, it is crucial for organizations to utilize advanced methods to reduce energy consumption and energy costs.This study explores the application of deep learning models for predicting energy demands in retail stores, which can enhance market efficiency and contribute to grid stability.We analyze a detailed electricity consumption dataset from a hypermarket in Hungary, focusing on 48-hour forecasts at 15-minute intervals.Our methodology includes the implementation of classical models such as ARIMA and linear regression, as well as state-of-the-art deep learning models like TiDE and foundational models such as Lag-Llama in a "zero shot prediction" as well as a "finetuning" scenario.