School buildings represent 17% of the European building stock and cause 12% of total energy consumption. In addition, they play a key social role. In Italy, 20% of the population studies or works in the over 65’000 schools distributed throughout the Nation. Our research start reviewing the of Italian school buildings asset, and then deepens, through the selection of a case study, which are the parameters that most influence energy demand. Using the IESVE 2019 energy modelling software, we conducted an uncertainties and sensitivities analysis, aimed to identify the most critical parameters, which need further investigation. Subsequently we monitored using sensors the carbon dioxide concentrations in the environment, the internal temperatures and the frequency of opening the windows in order to calibrate the energy model and reduce the amplitude of uncertainties. Finally, we hypothesized a requalification scenario, aimed to reduce the energy consumption and improve indoor environmental comfort. By improving the thermal performance of the building envelope, the primary energy consumption due to winter heating is reduced by 52,9%. By optimizing the temperature control system and the heating system, is obtained a further reduction of the primary energy required by the system by 71,5%. In general, the optimization process allows to reduce primary energy consumption from 144,66 kWhPE/m2y to 41,13 kWhPE/m2y, and to decrease the discomfort from 1’314 to 14 hours.
Gli edifici scolastici rappresentano il 17% del patrimonio edilizio europeo e sono causa del 12% dei consumi totali di energia. Inoltre, essi ricoprono un ruolo chiave a livello sociale. In Italia, il 20% della popolazione studia o lavora nelle oltre 65'000 scuole distribuite in tutta la Nazione. La nostra ricerca revisiona il patrimonio degli edifici scolastici italiani, per poi approfondire, attraverso la selezione di un caso di studio, quali siano i parametri che più influenzano la domanda energetica. Utilizzando il software per modellazioni energetiche IESVE 2019 abbiamo condotto un’analisi delle incertezze e delle sensibilità, finalizzate ad individuare i parametri più critici, che necessitano di ulteriori approfondimenti. Successivamente abbiamo monitorato attraverso dei sensori le concentrazioni di anidride carbonica in ambiente, le temperature interne e la frequenza di apertura delle finestre al fine di calibrare il modello energetico e ridurre l’ampiezza delle incertezze. Infine, abbiamo ipotizzato uno scenario di riqualifica, volto a ridurre i consumi energetici e migliorare le condizioni di comfort interno. Migliorando le prestazioni termiche dell’involucro edilizio si ottiene una riduzione dei consumi di energia primaria dovuti al riscaldamento invernale pari al 52,9%. Ottimizzando il sistema di controllo delle temperature e l’impianto di riscaldamento si ottiene un ulteriore riduzione dell’energia primaria richiesta dall’impianto del 71,5%. In generale il processo di ottimizzazione permette di ridurre i consumi di energia primaria da 144,66 kWhPE/m2y a 41,13 kWhPE/m2y, e diminuire le ore di discomfort da 1314 a 14.
Educational asset retrofit. A case study in Milano. How we started reviewing the Italian educational buildings asset and we decided to face a case study, understand its energy efficiency in a sensitivity analysis and tried quite a long list of simulation to understand the feasibility of an energy retrofit
BORSANI, OMAR;CATTANEO, SIMONE
2018/2019
Abstract
School buildings represent 17% of the European building stock and cause 12% of total energy consumption. In addition, they play a key social role. In Italy, 20% of the population studies or works in the over 65’000 schools distributed throughout the Nation. Our research start reviewing the of Italian school buildings asset, and then deepens, through the selection of a case study, which are the parameters that most influence energy demand. Using the IESVE 2019 energy modelling software, we conducted an uncertainties and sensitivities analysis, aimed to identify the most critical parameters, which need further investigation. Subsequently we monitored using sensors the carbon dioxide concentrations in the environment, the internal temperatures and the frequency of opening the windows in order to calibrate the energy model and reduce the amplitude of uncertainties. Finally, we hypothesized a requalification scenario, aimed to reduce the energy consumption and improve indoor environmental comfort. By improving the thermal performance of the building envelope, the primary energy consumption due to winter heating is reduced by 52,9%. By optimizing the temperature control system and the heating system, is obtained a further reduction of the primary energy required by the system by 71,5%. In general, the optimization process allows to reduce primary energy consumption from 144,66 kWhPE/m2y to 41,13 kWhPE/m2y, and to decrease the discomfort from 1’314 to 14 hours.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/154089