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Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses

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  • Prataviera, Enrico
  • Zarrella, Angelo
  • Morejohn, Joshua
  • Narayanan, Vinod

Abstract

Research effort in analyzing the energy consumption of buildings in districts or cities is increasing as new paradigms of distributed energy production and sharing are spreading. In this work, the Urban Building Energy Model of the Quad, an area of the University of California Davis campus, is presented, validated, and analyzed for possible actions to reduce the district cooling energy consumption. Energy conservation measures involve buildings' air handling units and district chilled water generation. To investigate this system, a district cooling networks module has been developed in EUReCA, the Urban Building Energy Modeling tool used for the analyses. The model has been validated with energy demand and temperature data from 2018 to 2020, resulting in a district cooling demand deviation lower than 7% for 2018 and 2019. Normalized Root Mean Square Error is lower than 35% for each building, proving the reliability at the hourly time scale. Systems energy reduction actions like heat recovery units' installation and heuristic control of the air supply cooling temperature are beneficial in reducing the cooling demand, and they allow a more efficient discharging of the chilled water storage, reducing the average electricity price by load shifting during peak demand.

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  • Prataviera, Enrico & Zarrella, Angelo & Morejohn, Joshua & Narayanan, Vinod, 2024. "Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017324
    DOI: 10.1016/j.apenergy.2023.122368
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    References listed on IDEAS

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    1. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    2. Hedegaard, Rasmus Elbæk & Kristensen, Martin Heine & Pedersen, Theis Heidmann & Brun, Adam & Petersen, Steffen, 2019. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response," Applied Energy, Elsevier, vol. 242(C), pages 181-204.
    3. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    4. Calama-González, Carmen María & Symonds, Phil & Petrou, Giorgos & Suárez, Rafael & León-Rodríguez, Ángel Luis, 2021. "Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring," Applied Energy, Elsevier, vol. 282(PA).
    5. Prataviera, Enrico & Romano, Pierdonato & Carnieletto, Laura & Pirotti, Francesco & Vivian, Jacopo & Zarrella, Angelo, 2021. "EUReCA: An open-source urban building energy modelling tool for the efficient evaluation of cities energy demand," Renewable Energy, Elsevier, vol. 173(C), pages 544-560.
    6. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    7. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    8. 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.
    9. Happle, Gabriel & Fonseca, Jimeno A. & Schlueter, Arno, 2020. "Impacts of diversity in commercial building occupancy profiles on district energy demand and supply," Applied Energy, Elsevier, vol. 277(C).
    10. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    11. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    12. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
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