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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Francisco G. Montoya & Alberto-Jesus Perea-Moreno, 2020. "Environmental Energy Sustainability at Universities," Sustainability, MDPI, vol. 12(21), pages 1-3, November.
    5. 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.
    6. 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.
    7. 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.

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