Mouakher, Amira, Inoubli, Wissem ORCID: https://orcid.org/0000-0001-5121-9043, Ounoughi, Chahinez ORCID: https://orcid.org/0000-0002-2063-2844 and Kő, Andrea ORCID: https://orcid.org/0000-0003-0023-1143 (2022) Expect: EXplainable Prediction Model for Energy ConsumpTion. Mathematics, 10 (2). DOI https://doi.org/10.3390/math10020248
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Official URL: https://doi.org/10.3390/math10020248
Abstract
With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called EXPECT, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.
Item Type: | Article |
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Uncontrolled Keywords: | time series forecasting, energy consumption, missing values, embeddings, long shortterm memory, explainable artificial intelligence |
Divisions: | Corvinus Institute for Advanced Studies (CIAS) |
Subjects: | Energy economy Mathematics, Econometrics |
DOI: | https://doi.org/10.3390/math10020248 |
ID Code: | 7156 |
Deposited By: | MTMT SWORD |
Deposited On: | 25 Jan 2022 17:29 |
Last Modified: | 04 Apr 2022 14:17 |
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