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Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

Author

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  • Cinzia Buratti

    (Department of Engineering, University of Perugia, Via G. Duranti 67, 06125 Perugia, Italy)

  • Elisa Lascaro

    (Department of Engineering, University of Perugia, Via G. Duranti 67, 06125 Perugia, Italy)

  • Domenico Palladino

    (Department of Engineering, University of Perugia, Via G. Duranti 67, 06125 Perugia, Italy)

  • Marco Vergoni

    (Department of Engineering, University of Perugia, Via G. Duranti 67, 06125 Perugia, Italy)

Abstract

Many studies in Italy showed that buildings are responsible for about 40% of total energy consumption, due to worsening performance of building envelope; in fact, a great number of Italian buildings were built before the 1970s and 80s. In particular, the energy consumptions for cooling are considerably increased with respect to the ones for heating. In order to reduce the cooling energy demand, ensuring indoor thermal comfort, a careful study on building envelope performance is necessary. Different dynamic software could be used in order to evaluate and to improve the building envelope during the cooling period, but much time and an accurate validation of the model are required. However, when a wide experimental data is available, the Artificial Neural Network (ANN) can be an alternative, simple and fast tool to use. In the present study, the indoor thermal conditions in many dwellings built in Umbria Region were investigated in order to evaluate the envelope performance. They were recently built and have very low energy consumptions. Based on the experimental data, a feed forward network was trained, in order to evaluate the different envelopes performance. As input parameters the outdoor climatic conditions and the thermal characteristics of building envelopes were set, while, as a target parameter, the indoor air temperature was provided. A good training of network was obtained with a high regression value (0.9625) and a very small error (0.007 °C) on air temperature. The network was also used to simulate the envelope behavior with new innovative glazing systems, in order to evaluate and to improve the energy performance.

Suggested Citation

  • Cinzia Buratti & Elisa Lascaro & Domenico Palladino & Marco Vergoni, 2014. "Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions," Sustainability, MDPI, vol. 6(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:6:y:2014:i:8:p:5339-5353:d:39288
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    References listed on IDEAS

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    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. Rossi, Federico & Pisello, Anna Laura & Nicolini, Andrea & Filipponi, Mirko & Palombo, Massimo, 2014. "Analysis of retro-reflective surfaces for urban heat island mitigation: A new analytical model," Applied Energy, Elsevier, vol. 114(C), pages 621-631.
    3. Buratti, C. & Moretti, E., 2012. "Experimental performance evaluation of aerogel glazing systems," Applied Energy, Elsevier, vol. 97(C), pages 430-437.
    4. Francesco Asdrubali & Cinzia Buratti & Franco Cotana & Giorgio Baldinelli & Michele Goretti & Elisa Moretti & Catia Baldassarri & Elisa Belloni & Francesco Bianchi & Antonella Rotili & Marco Vergoni &, 2013. "Evaluation of Green Buildings’ Overall Performance through in Situ Monitoring and Simulations," Energies, MDPI, vol. 6(12), pages 1-23, December.
    5. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    6. Anna Laura Pisello & Michael Bobker & Franco Cotana, 2012. "A Building Energy Efficiency Optimization Method by Evaluating the Effective Thermal Zones Occupancy," Energies, MDPI, vol. 5(12), pages 1-22, December.
    7. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    8. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    9. 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.
    10. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    11. Buratti, C. & Moretti, E., 2012. "Glazing systems with silica aerogel for energy savings in buildings," Applied Energy, Elsevier, vol. 98(C), pages 396-403.
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    Cited by:

    1. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Buratti, C. & Palladino, D. & Ricciardi, P., 2016. "Application of a new 13-value thermal comfort scale to moderate environments," Applied Energy, Elsevier, vol. 180(C), pages 859-866.
    3. Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    4. Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
    5. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    6. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    7. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.

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