IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v48y2012i1p398-405.html
   My bibliography  Save this article

Long term energy demand projection and potential for energy savings of Croatian tourism–catering trade sector

Author

Listed:
  • Irsag, Bojan
  • Pukšec, Tomislav
  • Duić, Neven

Abstract

Today, tourism represents one of the backbones of Croatian economy and one of the main factors of its growth. Combined with catering trade sector, tourism represents a significant energy consumer that has the tendencies of future growth. Since services sector, which tourism–catering trade sector is a part of, is not yet well described regarding future energy balances it would be very interesting to see how could possible future growth in tourism influence energy consumption of the services sector in Croatia. Through this paper long term energy demand projections of tourism–catering trade sector were studied with special emphasis on future growth of tourism in Croatia as well as different mechanisms that might lead to certain energy savings. Bottom-up approach was chosen as the most suitable one since it allows better quantification of different measures, technological or legal, that would influence future energy demand. Downside of this approach is extensive input data that is required to analyse and model future energy demand which is roughly divided into heating/cooling section and all other consumption. Results show that additional energy savings in the tourism–catering trade sector are possible if careful and rational demand side planning is in place.

Suggested Citation

  • Irsag, Bojan & Pukšec, Tomislav & Duić, Neven, 2012. "Long term energy demand projection and potential for energy savings of Croatian tourism–catering trade sector," Energy, Elsevier, vol. 48(1), pages 398-405.
    • Handle: RePEc:eee:energy:v:48:y:2012:i:1:p:398-405
    DOI: 10.1016/j.energy.2012.06.069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421200521X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2012.06.069?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. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Lai, T.M. & To, W.M. & Lo, W.C. & Choy, Y.S., 2008. "Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China," Energy, Elsevier, vol. 33(5), pages 679-688.
    3. Duic, Neven & Juretic, Franjo & Zeljko, Mladen & Bogdan, Zeljko, 2005. "Croatia energy planning and Kyoto Protocol," Energy Policy, Elsevier, vol. 33(8), pages 1003-1010, May.
    4. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    5. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
    6. Schiff, Aaron & Becken, Susanne, 2011. "Demand elasticity estimates for New Zealand tourism," Tourism Management, Elsevier, vol. 32(3), pages 564-575.
    7. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    8. Schneider, Daniel R. & Duić, Neven & Bogdan, Željko, 2007. "Mapping the potential for decentralized energy generation based on renewable energy sources in the Republic of Croatia," Energy, Elsevier, vol. 32(9), pages 1731-1744.
    9. Sohn, Ira, 2007. "Long-term energy projections: What lessons have we learned?," Energy Policy, Elsevier, vol. 35(9), pages 4574-4584, September.
    10. Lund, Henrik & Duić, Neven & Krajac˘ić, Goran & Graça Carvalho, Maria da, 2007. "Two energy system analysis models: A comparison of methodologies and results," Energy, Elsevier, vol. 32(6), pages 948-954.
    11. Koopmans, Carl C. & te Velde, Dirk Willem, 2001. "Bridging the energy efficiency gap: using bottom-up information in a top-down energy demand model," Energy Economics, Elsevier, vol. 23(1), pages 57-75, January.
    12. 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. Cerovac, Tin & Ćosić, Boris & Pukšec, Tomislav & Duić, Neven, 2014. "Wind energy integration into future energy systems based on conventional plants – The case study of Croatia," Applied Energy, Elsevier, vol. 135(C), pages 643-655.
    2. Djula Borozan & Mirjana Radman Funaric, 2018. "The Impact of Disaggregated Social Capital on Household Electricity Intensity," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 16(2), pages 189-207.
    3. Georgopoulou, E. & Mirasgedis, S. & Sarafidis, Y. & Gakis, N. & Hontou, V. & Lalas, D.P. & Steiner, D. & Tuerk, A. & Fruhmann, C. & Pucker, J., 2015. "Lessons learnt from a sectoral analysis of greenhouse gas mitigation potential in the Balkans," Energy, Elsevier, vol. 92(P3), pages 577-591.
    4. Syed Abdul Rehman Khan & Danish Iqbal Godil & Zhang Yu & Farwa Abbas & Muhammad Asif Shamim, 2022. "Adoption of renewable energy sources, low‐carbon initiatives, and advanced logistical infrastructure—an step toward integrated global progress," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 275-288, February.
    5. Fátima Calderón-Vargas & David Asmat-Campos & Anselmo Carretero-Gómez, 2019. "Sustainable Tourism and Renewable Energy: Binomial for Local Development in Cocachimba, Amazonas, Peru," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    6. Prebeg, Pero & Gasparovic, Goran & Krajacic, Goran & Duic, Neven, 2016. "Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 1493-1507.
    7. Federico Scarpa & Vincenzo Bianco, 2017. "Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector," Energies, MDPI, vol. 10(11), pages 1-13, November.
    8. Djula Borozan & Luka Borozan, 2018. "Analyzing total-factor energy efficiency in Croatian counties: evidence from a non-parametric approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 673-694, September.
    9. Vidakovic, Neven, 2014. "Transition towards renewable energy supply in Croatia," MPRA Paper 63957, University Library of Munich, Germany.
    10. Borović, Staša & Marković, Izidora, 2015. "Utilization and tourism valorisation of geothermal waters in Croatia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 52-63.
    11. Pukšec, Tomislav & Mathiesen, Brian Vad & Novosel, Tomislav & Duić, Neven, 2014. "Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia," Energy, Elsevier, vol. 76(C), pages 198-209.
    12. Komušanac, Ivan & Ćosić, Boris & Duić, Neven, 2016. "Impact of high penetration of wind and solar PV generation on the country power system load: The case study of Croatia," Applied Energy, Elsevier, vol. 184(C), pages 1470-1482.
    13. Francesco Calise & Mário Costa & Qiuwang Wang & Xiliang Zhang & Neven Duić, 2018. "Recent Advances in the Analysis of Sustainable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-30, September.

    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. 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.
    2. 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.
    3. 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.
    4. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    5. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    6. 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).
    7. Günay, M. Erdem, 2016. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey," Energy Policy, Elsevier, vol. 90(C), pages 92-101.
    8. Cinar, Didem & Kayakutlu, Gulgun & Daim, Tugrul, 2010. "Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey," Energy, Elsevier, vol. 35(4), pages 1724-1729.
    9. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
    10. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    11. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    12. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    13. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    14. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    15. Changrui Deng & Xiaoyuan Zhang & Yanmei Huang & Yukun Bao, 2021. "Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting," Energies, MDPI, vol. 14(13), pages 1-14, July.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    18. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    19. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    20. Bašta, Milan & Helman, Karel, 2013. "Scale-specific importance of weather variables for explanation of variations of electricity consumption: The case of Prague, Czech Republic," Energy Economics, Elsevier, vol. 40(C), pages 503-514.

    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:48:y:2012:i:1:p:398-405. 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.