Application of Artificial Intelligence for Predicting CO 2 Emission Using Weighted Multi-Task Learning

Mohammad Talaei, Majid Astaneh, Elmira Ghiasabadi Farahani and Farzin Golzar ()
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Mohammad Talaei: Department of Energy Engineering, Sharif University of Technology, Tehran 1458889694, Iran
Majid Astaneh: Northvolt Battery Systems AB, Alströmergatan 20, 112 47 Stockholm, Sweden
Elmira Ghiasabadi Farahani: Department of Energy Engineering, Sharif University of Technology, Tehran 1458889694, Iran
Farzin Golzar: Department of Energy Technology, KTH-Royal Institute of Technology, 114 28 Stockholm, Sweden

Energies, 2023, vol. 16, issue 16, 1-18

Abstract: Carbon emissions significantly contribute to global warming, amplifying the occurrence of extreme weather events and negatively impacting the overall environmental transformation. In line with the global commitment to combat climate change through the Paris Agreement (COP21), the European Union (EU) has formulated strategies aimed at achieving climate neutrality by 2050. To achieve this goal, EU member states focus on developing long-term national strategies (NLTSs) and implementing local plans to reduce greenhouse gas (GHG) emissions in alignment with EU objectives. This study focuses on the case of Sweden and aims to introduce a comprehensive data-driven framework that predicts CO 2 emissions by using a diverse range of input features. Considering the scarcity of data points, we present a refined variation of multi-task learning (MTL) called weighted multi-task learning (WMTL). The findings demonstrate the superior performance of the WMTL model in terms of accuracy, robustness, and computation cost of training compared to both the basic model and MTL model. The WMTL model achieved an average mean squared error (MSE) of 0.12 across folds, thus outperforming the MTL model’s 0.15 MSE and the basic model’s 0.21 MSE. Furthermore, the computational cost of training the new model is only 20% of the cost required by the other two models. The findings from the interpretation of the WMTL model indicate that it is a promising tool for developing data-driven decision-support tools to identify strategic actions with substantial impacts on the mitigation of CO 2 emissions.

Keywords: CO 2 emissions prediction; artificial intelligence; weighted multi-task learning (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: 2023
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