IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v196y2020ics0360544220301651.html
   My bibliography  Save this article

Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models

Author

Listed:
  • Di Leo, Senatro
  • Caramuta, Pietro
  • Curci, Paola
  • Cosmi, Carmelina

Abstract

A reliable energy supply is fundamental to ensure energy security and support the mitigation of climate change by promoting the use of renewable sources and reducing carbon emissions. Energy system analysis provides a sound methodology to assess energy needs, allowing to investigate the energy system behavior and to individuate the optimal energy-technology configurations for the achievement of strategic energy and environmental policy targets. In this framework, the estimation of future trends of exogenous variables such as energy demand has a fundamental importance to obtain reliable and effective solutions, contributing remarkably to the accuracy of models’ input data. This study illustrates an application of regression analysis to predict energy demand trends in end use sectors. The proposed procedure is applied to characterize statistically the relationships between population and gross domestic product (independent variables) and energy demands of Residential, Transport and Commercial in order to determine the energy demand trends over a long-term horizon. The effectiveness of linear and nonlinear regression models for energy demand forecasting has been validated by classical statistical tests. Energy demand projections have been tested as input data of the bottom-up TIMES model in two applications (the TIMES-Basilicata and TIMES-Italy models) confirming the validity of the forecasting approach.

Suggested Citation

  • Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301651
    DOI: 10.1016/j.energy.2020.117058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220301651
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "Rejoinder on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 442-447, September.
    2. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Richard Loulou & Maryse Labriet, 2008. "ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure," Computational Management Science, Springer, vol. 5(1), pages 7-40, February.
    5. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    6. Comodi, Gabriele & Cioccolanti, Luca & Renzi, Massimiliano, 2014. "Modelling the Italian household sector at the municipal scale: Micro-CHP, renewables and energy efficiency," Energy, Elsevier, vol. 68(C), pages 92-103.
    7. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    8. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    9. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    10. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    11. Richard Loulou, 2008. "ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation," Computational Management Science, Springer, vol. 5(1), pages 41-66, February.
    12. Adeoye, Omotola & Spataru, Catalina, 2019. "Modelling and forecasting hourly electricity demand in West African countries," Applied Energy, Elsevier, vol. 242(C), pages 311-333.
    13. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    14. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    15. Alain Bernard & Marc Vielle, 2008. "GEMINI-E3, a general equilibrium model of international–national interactions between economy, energy and the environment," Computational Management Science, Springer, vol. 5(3), pages 173-206, May.
    16. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    17. Köne, Aylin Çigdem & Büke, Tayfun, 2010. "Forecasting of CO2 emissions from fuel combustion using trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2906-2915, December.
    18. Di Leo, Senatro & Pietrapertosa, Filomena & Loperte, Simona & Salvia, Monica & Cosmi, Carmelina, 2015. "Energy systems modelling to support key strategic decisions in energy and climate change at regional scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 394-414.
    19. Cosmi, C. & Di Leo, S. & Loperte, S. & Macchiato, M. & Pietrapertosa, F. & Salvia, M. & Cuomo, V., 2009. "A model for representing the Italian energy system: The NEEDS-TIMES experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 763-776, May.
    20. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    21. Gouveia, João Pedro & Fortes, Patrícia & Seixas, Júlia, 2012. "Projections of energy services demand for residential buildings: Insights from a bottom-up methodology," Energy, Elsevier, vol. 47(1), pages 430-442.
    22. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    23. P. Capros & Denise Van Regemorter & Leonidas Paroussos & P. Karkatsoulis & C. Fragkiadakis & S. Tsani & I. Charalampidis & Tamas Revesz, 2013. "GEM-E3 Model Documentation," JRC Research Reports JRC83177, Joint Research Centre.
    24. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    2. M'Saouri El Bat, Adnane & Romani, Zaid & Bozonnet, Emmanuel & Draoui, Abdeslam & Allard, Francis, 2023. "Optimizing urban courtyard form through the coupling of outdoor zonal approach and building energy modeling," Energy, Elsevier, vol. 264(C).
    3. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
    4. Zaman Sajid & Asma Javaid & Muhammad Kashif Khan & Hamad Sadiq & Usman Hamid, 2021. "Integration of Regression Analysis and Monte Carlo Simulation for Probabilistic Energy Policy Guidelines in Pakistan," Resources, MDPI, vol. 10(9), pages 1-26, August.
    5. Besagni, Giorgio & Borgarello, Marco & Premoli Vilà, Lidia & Najafi, Behzad & Rinaldi, Fabio, 2020. "MOIRAE – bottom-up MOdel to compute the energy consumption of the Italian REsidential sector: Model design, validation and evaluation of electrification pathways," Energy, Elsevier, vol. 211(C).
    6. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    7. Chang, Miguel & Thellufsen, Jakob Zink & Zakeri, Behnam & Pickering, Bryn & Pfenninger, Stefan & Lund, Henrik & Østergaard, Poul Alberg, 2021. "Trends in tools and approaches for modelling the energy transition," Applied Energy, Elsevier, vol. 290(C).
    8. Maaouane, Mohamed & Zouggar, Smail & Krajačić, Goran & Zahboune, Hassan, 2021. "Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods," Energy, Elsevier, vol. 225(C).
    9. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    10. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    11. Shane D. Sanders, 2024. "Wages, Talent, and Demand for NCAA Sport After the Alston v. NCAA Antitrust Case," Journal of Sports Economics, , vol. 25(2), pages 169-185, February.
    12. Cai, Liya & Luo, Ji & Wang, Minghui & Guo, Jianfeng & Duan, Jinglin & Li, Jingtao & Li, Shuo & Liu, Liting & Ren, Dangpei, 2023. "Pathways for municipalities to achieve carbon emission peak and carbon neutrality: A study based on the LEAP model," Energy, Elsevier, vol. 262(PB).
    13. Nurkhat Zhakiyev & Ayagoz Khamzina & Svetlana Zhakiyeva & Rocco De Miglio & Aidyn Bakdolotov & Carmelina Cosmi, 2023. "Optimization Modelling of the Decarbonization Scenario of the Total Energy System of Kazakhstan until 2060," Energies, MDPI, vol. 16(13), pages 1-14, July.
    14. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    15. Di Leo, Senatro & Pietrapertosa, Filomena & Salvia, Monica & Cosmi, Carmelina, 2021. "Contribution of the Basilicata region to decarbonisation of the energy system: results of a scenario analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    16. Shoaib Ahmed Khatri & Nayyar Hussain Mirjat & Khanji Harijan & Mohammad Aslam Uqaili & Syed Feroz Shah & Pervez Hameed Shaikh & Laveet Kumar, 2022. "An Overview of the Current Energy Situation of Pakistan and the Way Forward towards Green Energy Implementation," Energies, MDPI, vol. 16(1), pages 1-27, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maaouane, Mohamed & Zouggar, Smail & Krajačić, Goran & Zahboune, Hassan, 2021. "Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods," Energy, Elsevier, vol. 225(C).
    2. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    4. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    5. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    6. Di Leo, Senatro & Pietrapertosa, Filomena & Salvia, Monica & Cosmi, Carmelina, 2021. "Contribution of the Basilicata region to decarbonisation of the energy system: results of a scenario analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    8. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    9. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    10. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    11. Blanco, Herib & Gómez Vilchez, Jonatan J. & Nijs, Wouter & Thiel, Christian & Faaij, André, 2019. "Soft-linking of a behavioral model for transport with energy system cost optimization applied to hydrogen in EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    12. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    13. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, vol. 12(19), pages 1-22, September.
    14. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    15. Maryse Labriet & Santosh Joshi & Marc Vielle & Philip Holden & Neil Edwards & Amit Kanudia & Richard Loulou & Frédéric Babonneau, 2015. "Worldwide impacts of climate change on energy for heating and cooling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(7), pages 1111-1136, October.
    16. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    17. Welsch, M. & Howells, M. & Bazilian, M. & DeCarolis, J.F. & Hermann, S. & Rogner, H.H., 2012. "Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code," Energy, Elsevier, vol. 46(1), pages 337-350.
    18. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
    19. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    20. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301651. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.