Expect: EXplainable Prediction Model for Energy ConsumpTion

Mouakher, Amira, Inoubli, Wissem ORCID:, Ounoughi, Chahinez ORCID: and Kő, Andrea ORCID: (2022) Expect: EXplainable Prediction Model for Energy ConsumpTion. Mathematics, 10 (2). DOI

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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
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
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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