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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Spyros Giannelos & Alexandre Moreira & Dimitrios Papadaskalopoulos & Stefan Borozan & Danny Pudjianto & Ioannis Konstantelos & Mingyang Sun & Goran Strbac, 2023. "A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector," Energies, MDPI, vol. 16(6), pages 1-37, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2915-:d:1104155
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    References listed on IDEAS

    as
    1. Spyros Giannelos & Stefan Borozan & Goran Strbac, 2022. "A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty," Energies, MDPI, vol. 15(9), pages 1-22, May.
    2. Spyros Giannelos & Predrag Djapic & Danny Pudjianto & Goran Strbac, 2020. "Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology," Energies, MDPI, vol. 13(4), pages 1-15, February.
    3. Pradyot Ranjan Jena & Shunsuke Managi & Babita Majhi, 2021. "Forecasting the CO 2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. Spyros Giannelos & Anjali Jain & Stefan Borozan & Paola Falugi & Alexandre Moreira & Rohit Bhakar & Jyotirmay Mathur & Goran Strbac, 2021. "Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty," Energies, MDPI, vol. 14(22), pages 1-27, November.
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    Cited by:

    1. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
    2. Hamza Mubarak & Mohammad J. Sanjari & Sascha Stegen & Abdallah Abdellatif, 2023. "Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study," Energies, MDPI, vol. 16(21), pages 1-32, October.
    3. Francois Rozon & Craig McGregor & Michael Owen, 2023. "Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050," Energies, MDPI, vol. 16(19), pages 1-20, September.

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