Transformer-Based Model for Electrical Load Forecasting

Alexandra L’Heureux, Katarina Grolinger and Miriam A. M. Capretz
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Alexandra L’Heureux: Department of Electrical and Computer Engineering, The University of Western Ontario, London, ON N6A 5B9, Canada
Katarina Grolinger: Department of Electrical and Computer Engineering, The University of Western Ontario, London, ON N6A 5B9, Canada
Miriam A. M. Capretz: Department of Electrical and Computer Engineering, The University of Western Ontario, London, ON N6A 5B9, Canada

Energies, 2022, vol. 15, issue 14, 1-23

Abstract: Amongst energy-related CO 2 emissions, electricity is the largest single contributor, and with the proliferation of electric vehicles and other developments, energy use is expected to increase. Load forecasting is essential for combating these issues as it balances demand and production and contributes to energy management. Current state-of-the-art solutions such as recurrent neural networks (RNNs) and sequence-to-sequence algorithms (Seq2Seq) are highly accurate, but most studies examine them on a single data stream. On the other hand, in natural language processing (NLP), transformer architecture has become the dominant technique, outperforming RNN and Seq2Seq algorithms while also allowing parallelization. Consequently, this paper proposes a transformer-based architecture for load forecasting by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features. Moreover, in contrast to most load forecasting studies, we evaluate the proposed solution on different data streams under various forecasting horizons and input window lengths in order to ensure result reproducibility. Results show that the proposed approach successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.

Keywords: electrical load forecasting; deep learning; transformer architecture; machine learning; sequence-to-sequence model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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