Open Access
E3S Web Conf.
Volume 197, 2020
75th National ATI Congress – #7 Clean Energy for all (ATI 2020)
Article Number 03001
Number of page(s) 14
Section Management of Energy Supply and Demand. Smart Grid
Published online 22 October 2020
  1. International Energy Agency (IEA), Global status report for buildings and construction 2019, IEA, Paris (2019), available at: [accessed on September 2, 2020] [Google Scholar]
  2. T. Fleiter, R. Elsland, M. Rehfeldt, J. Steinbach, U. Reiter, G. Catenazzi, M. Jakob, C. Rutten, R. Harmsen, F. Dittmann, P. Riviére, P. Stabat, Profile of heating and cooling demand in 2015, D3.1 report, Heat Roadmap Europe 2050, A low-carbon heating and cooling strategy (2017), available at: [accessed on September 2, 2020] [Google Scholar]
  3. D. Testi, M. Rocca, E. Menchetti, S. Comelato, Criticalities in the NZEB retrofit of scholastic buildings: analysis of a secondary school in Centre Italy, Energy Procedia, 140, 252-264 (2017) [CrossRef] [Google Scholar]
  4. S. Della Torre, M. Bocciarelli, L. Daglio, R. Neri, Buildings for education – a multidisciplinary overview of the design of school buildings, Springer (2020) [CrossRef] [Google Scholar]
  5. M. Dovjak, A. Kukec, Creating healthy and sustainable buildings, Springer (2019) [CrossRef] [Google Scholar]
  6. L. Kaufman, P.J. Rousseeuw, Finding groups in data – an introduction to cluster analysis, John Wiley & Sons (2005) [Google Scholar]
  7. G. Gan, C. Ma, J. Wu, Data clustering: theory, algorithms, and applications, ASA-SIAM Series on Statistics and Applied Probability (2007) [Google Scholar]
  8. J. Yang, C. Ning, C. Deb, F. Zhang, D. Cheong, S.E. Lee, C. Sekhar, K. Tham, k-shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement, Energy and Buildings, 146, 27-37 (2017) [CrossRef] [Google Scholar]
  9. A. Zakovorotnyi, A. Seerig, Building energy data analysis by clustering measured daily profiles, Energy Procedia, 122, 583-588 (2017) [CrossRef] [Google Scholar]
  10. P. Marrone, P. Gori, F. Asdrubali, L. Evangelisti, L. Calcagnini, G. Grazieschi, Energy benchmarking in educational buildings through cluster analysis of energy retrofitting, Energies, 11, pp. 20-649 (2018) [CrossRef] [Google Scholar]
  11. M. Santamouris, G. Mihalakakou, P. Patargias, N. Gaitani, K. Sfakianaki, M. Papaglastra, C. Pavlou, P. Doukas, E. Primikiri, V. Geros, M.N. Assimakopoulos, R. Mitoula, S. Zerefos, Using intelligent clustering techniques to classify the energy performance of school buildings, Energy and Buildings, 39, 45-51 (2007) [CrossRef] [Google Scholar]
  12. K. Li, Z. Ma, D. Robinson, J. Ma, A two-level clustering strategy for energy performance evaluation of university buildings, Proceedings of the 4th International Conference on Building Energy and Environment (COBEE 2018), Melbourne, Australia, Paper 061, 168-173 (2018) [Google Scholar]
  13. E. Schito, P. Conti, L. Urbanucci, D. Testi, Multi-objective optimization of HVAC control in museum environment for artwork preservation, visitors’ thermal comfort and energy efficiency, Building and Environment, 180, 107018, 15 pp. (2020). [CrossRef] [Google Scholar]
  14. P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Computational and Applied Mathematics, 20, 53-65 (1987) [CrossRef] [Google Scholar]
  15. D.B. Crawley, J.W. Hand, M. Kummert, B.T. Griffith, Contrasting the capabilities of building energy performance simulation programs, Building and Environment, 43, 66173 (2008) [CrossRef] [Google Scholar]
  16. D. Testi, E. Schito, E. Tiberi, P. Conti, W. Grassi, Building energy simulation by an inhouse full transient model for radiant systems coupled to a modulating heat pump, Energy Procedia, 78, 1135-1140 (2015) [CrossRef] [Google Scholar]
  17. E. Schito, D. Testi, Integrated maps of risk assessment and minimization of multiple risks for artworks in museum environments based on microclimate control, Building and Environment, 123, 585-600 (2017) [CrossRef] [Google Scholar]
  18. R. Gelaro and other 30 authors, The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), Journal of Climate, 30, 5419-5454 (2017) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  19. P.W. O’Callaghan, S.D. Probert, Technical note – sol-air temperature, Applied Energy, 3, 307-311 (1977) [CrossRef] [Google Scholar]
  20. D. Testi, E. Schito, P. Conti, Cost-optimal sizing of solar thermal and photovoltaic systems for the heating and cooling needs of a Nearly Zero-Energy Building: design methodology and model description, Energy Procedia, 91, 517-527 (2016) [CrossRef] [Google Scholar]
  21. A. Franco, E. Schito, Definition of optimal ventilation rates for balancing comfort and energy use in indoor spaces using CO2 concentration data, Buildings, 10, 135, 19 pp. (2020) [CrossRef] [Google Scholar]
  22. F. D’Ettorre, P. Conti, E. Schito, D. Testi, Model predictive control of a hybrid heat pump system and impact of the prediction horizon on cost-saving potential and optimal storage capacity, Applied Thermal Engineering, 148, 524-535 (2019) [CrossRef] [Google Scholar]
  23. F. D’Ettorre, M. De Rosa, P. Conti, D. Testi, D. Finn, Mapping the energy flexibility potential of single buildings equipped with optimally-controlled heat pump, gas boilers and thermal storage, Sustainable Cities and Society, 50, 101-689, 13 pp. (2019) [Google Scholar]
  24. L. Urbanucci, F. D’Ettorre, D. Testi, A comprehensive methodology for the integrated optimal sizing and operation of cogeneration systems with thermal energy storage, Energies, 12, 875, 17 pp. (2019) [CrossRef] [Google Scholar]
  25. L. Gigoni, A. Betti, E. Crisostomi, A. Franco, M. Tucci, F. Bizzarri, D. Mucci, Dayahead hourly forecasting of power generation from photovoltaic plants, IEEE Transactions on Sustainable Energy, 9, 831-842 (2017) [CrossRef] [Google Scholar]
  26. C. Bartoli, P. Conti, A. Franco, D. Testi, Experimental analysis of an air heat pump for heating service using a “hardware-in-the-loop” system, Energies, 13, 4498, 18 pp. (2020) [CrossRef] [Google Scholar]
  27. L. Schibuola, M. Scarpa, C. Tambani, CO2-based ventilation control in energy retrofit: an experimental assessment, Energy, 143, 606-614 (2018) [CrossRef] [Google Scholar]
  28. A. Franco, F. Leccese, Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes, Journal of Building Engineering, 101714 (2020), available online ( [CrossRef] [Google Scholar]
  29. UNI, Air-conditioning systems for thermal comfort in buildings – General, classification and requirements – Offer, order and supply specifications, Standard UNI 10339 (in Italian), Italian National Agency for Unification (1995) [Google Scholar]
  30. X. Xie, Q. Xue, Y. Zhou, K. Zhu, Q. Liu, J. Zhang, R. Song, Mental health status among children in home confinement during the coronavirus disease 2019 outbreak in Hubei province, China, JAMA Pediatrics, Research Letter, April 24, E1-E3 (2020) [Google Scholar]
  31. M. Poletti, A. Raballo, Evidence on school closure and children’s social contact: useful for coronavirus disease (COVID-19)?, Eurosurveillance, 25, Letter to the editor, April 30, 1-2 (2020) [Google Scholar]
  32. E. Schito, D. Testi, W. Grassi, A proposal for new microclimate indexes for the evaluation of indoor air quality in museums, Buildings, 6, 41, 15 pp. (2016) [CrossRef] [Google Scholar]
  33. E. Schito, P. Conti, D. Testi, Multi-objective optimization of microclimate in museums for concurrent reduction of energy needs, visitors’ discomfort and artwork preservation risks, Applied Energy, 224, 147-159 (2018) [CrossRef] [Google Scholar]
  34. A. Franco, Balancing user comfort and energy efficiency in public buildings through social interaction by ICT systems, Systems, 8, 29, 16 pp. (2020) [CrossRef] [Google Scholar]

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