IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1336-d319664.html
   My bibliography  Save this article

Benchmarking Energy Use at University of Almeria (Spain)

Author

Listed:
  • Mehdi Chihib

    (Department of Engineering, CEIA3, University of Almeria, 04120 Almeria, Spain)

  • Esther Salmerón-Manzano

    (Faculty of Law, Universidad Internacional de La Rioja (UNIR), Av. de la Paz, 137, 26006 Logroño, Spain)

  • Francisco Manzano-Agugliaro

    (Department of Engineering, CEIA3, University of Almeria, 04120 Almeria, Spain)

Abstract

Several factors impact the energy use of university campus buildings. This study aims to benchmark the energy use in universities with Mediterranean climates. The University of Almeria campus was used as a case study, and different types of buildings were analyzed. The second goal was to model the electricity consumption and determinate which parameter correlate strongly with energy use. Macro-scale energy consumption data during a period of seven years were gathered alongside cross-sectional buildings information. Eight years of daily outdoor temperature data were recorded and stored for every half hour. This dataset was eventually used to calculate heating and cooling degree-days. The weather factor was recognized as the variable with the greatest impact on campus energy consumption, and as the coefficient indicated a strong correlation, a linear regression model was established to forecast future energy use. A threshold of 8 GWh has been estimated as the energy consumption limit to be achieved despite the growth of the university. Finally, it is based on the results to inform the recommendations for decision making in order to act effectively to optimize and achieve a return on investment.

Suggested Citation

  • Mehdi Chihib & Esther Salmerón-Manzano & Francisco Manzano-Agugliaro, 2020. "Benchmarking Energy Use at University of Almeria (Spain)," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1336-:d:319664
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1336/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/4/1336/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    2. Guillermo Escrivá-Escrivá & Carlos Roldán-Blay & Carlos Roldán-Porta & Xavier Serrano-Guerrero, 2019. "Occasional Energy Reviews from an External Expert Help to Reduce Building Energy Consumption at a Reduced Cost," Energies, MDPI, vol. 12(15), pages 1-14, July.
    3. Manzano-Agugliaro, Francisco & Montoya, Francisco G. & Sabio-Ortega, Andrés & García-Cruz, Amós, 2015. "Review of bioclimatic architecture strategies for achieving thermal comfort," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 736-755.
    4. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2018. "Artificial neural networks and linear regression prediction models for social housing allocation: Fuel Poverty Potential Risk Index," Energy, Elsevier, vol. 164(C), pages 627-641.
    5. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    6. Yi-Tui Chen, 2017. "The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    7. Pilkington, Brian & Roach, Richard & Perkins, James, 2011. "Relative benefits of technology and occupant behaviour in moving towards a more energy efficient, sustainable housing paradigm," Energy Policy, Elsevier, vol. 39(9), pages 4962-4970, September.
    8. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.
    9. 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.
    10. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    11. Eric Forcael & Alberto Nope & Rodrigo García-Alvarado & Ariel Bobadilla & Carlos Rubio-Bellido, 2019. "Architectural and Management Strategies for the Design, Construction and Operation of Energy Efficient and Intelligent Primary Care Centers in Chile," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    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. Aiman Albatayneh & Mohammed N. Assaf & Renad Albadaineh & Adel Juaidi & Ramez Abdallah & Alberto Zabalo & Francisco Manzano-Agugliaro, 2022. "Reducing the Operating Energy of Buildings in Arid Climates through an Adaptive Approach," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    2. Jaqueline Litardo & Ruben Hidalgo-Leon & Guillermo Soriano, 2021. "Energy Performance and Benchmarking for University Classrooms in Hot and Humid Climates," Energies, MDPI, vol. 14(21), pages 1-17, October.
    3. Abdulaziz Alghamdi & Guangji Hu & Husnain Haider & Kasun Hewage & Rehan Sadiq, 2020. "Benchmarking of Water, Energy, and Carbon Flows in Academic Buildings: A Fuzzy Clustering Approach," Sustainability, MDPI, vol. 12(11), pages 1-25, May.
    4. Mehdi Chihib & Esther Salmerón-Manzano & Mimoun Chourak & Alberto-Jesus Perea-Moreno & Francisco Manzano-Agugliaro, 2021. "Impact of the COVID-19 Pandemic on the Energy Use at the University of Almeria (Spain)," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    5. Jihan Muhaidat & Aiman Albatayneh & Mohammed N. Assaf & Adel Juaidi & Ramez Abdallah & Francisco Manzano-Agugliaro, 2021. "The Significance of Occupants’ Interaction with Their Environment on Reducing Cooling Loads and Dermatological Distresses in East Mediterranean Climates," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    6. Francisco G. Montoya & Alberto-Jesus Perea-Moreno, 2020. "Environmental Energy Sustainability at Universities," Sustainability, MDPI, vol. 12(21), pages 1-3, November.
    7. Faouzan Abdulaziz Alfaoyzan & Radwan A. Almasri, 2023. "Benchmarking of Energy Consumption in Higher Education Buildings in Saudi Arabia to Be Sustainable: Sulaiman Al-Rajhi University Case," Energies, MDPI, vol. 16(3), pages 1-28, January.

    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. Hamid R. Khosravani & María Del Mar Castilla & Manuel Berenguel & Antonio E. Ruano & Pedro M. Ferreira, 2016. "A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building," Energies, MDPI, vol. 9(1), pages 1-24, January.
    2. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    3. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    4. Rahman, Aowabin & Smith, Amanda D., 2018. "Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms," Applied Energy, Elsevier, vol. 228(C), pages 108-121.
    5. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
    6. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    7. Elizabeth Hewitt & Yiyi Wang, 2020. "Understanding the Drivers of National-Level Energy Audit Behavior: Demographics and Socioeconomic Characteristics," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
    8. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    9. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    10. Tomasz Szul & Krzysztof Nęcka & Thomas G. Mathia, 2020. "Neural Methods Comparison for Prediction of Heating Energy Based on Few Hundreds Enhanced Buildings in Four Season’s Climate," Energies, MDPI, vol. 13(20), pages 1-17, October.
    11. Sharif Shofirun Sharif Ali & Muhammad Rizal Razman & Azahan Awang & M. R. M. Asyraf & M. R. Ishak & R. A. Ilyas & Roderick John Lawrence, 2021. "Critical Determinants of Household Electricity Consumption in a Rapidly Growing City," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    12. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
    13. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    14. 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).
    15. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    16. Yu, Xinran & Ergan, Semiha, 2022. "Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models," Applied Energy, Elsevier, vol. 310(C).
    17. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    18. Mardones, Cristian, 2021. "Ex-post evaluation and cost-benefit analysis of a heater replacement program implemented in southern Chile," Energy, Elsevier, vol. 227(C).
    19. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    20. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.

    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:gam:jsusta:v:12:y:2020:i:4:p:1336-:d:319664. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.