A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

Spyros Giannelos (), Alexandre Moreira, Dimitrios Papadaskalopoulos, Stefan Borozan, Danny Pudjianto, Ioannis Konstantelos, Mingyang Sun and Goran Strbac
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Spyros Giannelos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Alexandre Moreira: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Dimitrios Papadaskalopoulos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Stefan Borozan: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Danny Pudjianto: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Ioannis Konstantelos: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Mingyang Sun: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Goran Strbac: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK

Energies, 2023, vol. 16, issue 6, 1-37

Abstract: The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO 2 ) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO 2 emissions. Given the uncertainty around the future level of the corresponding CO 2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.

Keywords: ARIMA; deep learning; linear regression; machine learning; neural networks; uncertainty (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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