Expect: EXplainable Prediction Model for Energy ConsumpTion

Mathematics

Wissem Inoubli¹  Chahinez Ounoughi¹  Amira Mouakher²  Andrea Ko² 

[1] Department of Software Science, Tallinn University of Technology, 12618 Tallinn, Estonia;IT Institute, Corvinus University of Budapest, 1093 Budapest, Hungary;

关键词: time series forecasting;    energy consumption;    missing values;    embeddings;    long short-term memory;    explainable artificial intelligence;   

DOI  :  10.3390/math10020248

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown