2022
DOI: 10.1016/j.apenergy.2022.119278
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Estimating dynamic solar gains from on-site measured data: An ARX modelling approach

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Cited by 14 publications
(13 citation statements)
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“…For the details of measurement campaigns, the reader may refer to [17], pages 55-77 (CIEMAT case), and the images of measurement campaigns are shown in Figure 2. In line with [14,18], only indoor temperature (°C), outdoor temperature (°C), heating input (W), global horizontal irradiance (GHI) (W/m 2 ), direct normal irradiance (DNI) (W/m 2 ), and diffuse horizontal irradiance (DHI) (W/m 2 ), and wind speed (m/s) are included in the dataset, which is re-sampled from original 5 mins sampling time into a 20 mins frequency. The indoor temperature and heating input (in the ROBLS test) are measured in the box and the rest of the input weather data are recorded in a weather station on site [17].…”
Section: A Test Box As Case Studymentioning
confidence: 82%
“…For the details of measurement campaigns, the reader may refer to [17], pages 55-77 (CIEMAT case), and the images of measurement campaigns are shown in Figure 2. In line with [14,18], only indoor temperature (°C), outdoor temperature (°C), heating input (W), global horizontal irradiance (GHI) (W/m 2 ), direct normal irradiance (DNI) (W/m 2 ), and diffuse horizontal irradiance (DHI) (W/m 2 ), and wind speed (m/s) are included in the dataset, which is re-sampled from original 5 mins sampling time into a 20 mins frequency. The indoor temperature and heating input (in the ROBLS test) are measured in the box and the rest of the input weather data are recorded in a weather station on site [17].…”
Section: A Test Box As Case Studymentioning
confidence: 82%
“…The assumption of a constant solar opening coefficient is insufficient to take the solar gains into account. Modelling the solar gains must therefore be improved with a more detailed consideration of the position of the sun and the geometry of the windows, for example [14].…”
Section: Discussionmentioning
confidence: 99%
“…ML models fundamentally function as black-box models [81], which require no physical information of the system for modeling. The blackbox model is different from the grey-box [45,47,82] and white-box [83] models, where physical knowledge plays a vital role in model construction.…”
Section: Supervised Learning (Sl)mentioning
confidence: 99%