From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

Conference · Thu Oct 10 04:00:00 EDT 2024

OSTI ID:2551760

Bose, Shourya; Li, Yijiang; Van Sant, Amy; Zhang, Yu; Kim, Kibaek

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2551760
• Report Number(s): NREL/CP-5500-92331; MainId:94112; UUID:1c67d45a-6a08-4e4e-91eb-1822d644e505; MainAdminId:75871
• Country of Publication: United States
• Language: English

Related Subjects

ComStock
ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
commercial buildings
foundation models
load profiles
short-term load forecasting
smart grid operations