Expect: EXplainable Prediction Model for Energy ConsumpTion

Amira Mouakher, Wissem Inoubli, Chahinez Ounoughi and Andrea Kő
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Amira Mouakher: IT Institute, Corvinus University of Budapest, 1093 Budapest, Hungary
Wissem Inoubli: Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia
Chahinez Ounoughi: Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia

Mathematics, 2022, vol. 10, issue 2, 1-21

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.

Keywords: time series forecasting; energy consumption; missing values; embeddings; long short-term memory; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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