Advancing electric demand forecasting through the temporal fusion transformer model

Article type: Research Article

Authors: Karthikeyan, M.^a | Colak, Ilhami^{b; *} | Sagar Imambi, S.^c | Joselin Jeya Sheela, J.^d | Nair, Sruthi^e | Umarani, B.^f | Alagusabai, Andril^g | Suriyakrishnaan, K.^h | Rajaram, A.ⁱ

Affiliations: [a] Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, India | [b] Department of Electrical and Electronics Engineering, Istanbul, Turkiye, Nisantasi University, Turkey | [c] Department of Computer Science and Engineering, Koneru lakshmaih Education Foundation, Vaddeswaram, AndhraPradesh, India | [d] Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India | [e] Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India | [f] Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India | [g] Department of Electrical and Electronics Engineering, Bannari Amman Institue of Technology, Sathyamangalam, Tamil Nadu, India | [h] Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India | [i] Department of Electronics and Communication Engineering, EGS Pillay Engineering College, Nagapattinam, Tamil Nadu, India

Correspondence: [*] Corresponding author. Ilhami Colak, Department of Electrical and Electronics Engineering, Istanbul, Turkiye, Nisantasi University, Turkey. E-mail:[email protected].

Abstract: This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility.

Keywords: Electric demand forecasting, temporal fusion transformer, energy management, time-series analysis, transformer architecture

DOI: 10.3233/JIFS-236036

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024