This paper proposed a multiple regression model that estimated the indoor air temperature using the outdoor air and the inlet water temperatures, as well as the water flow rate, as independent variables. We also
Temperature check for guests and staff Dining, drinking, and snacking Shared kitchen Services and conveniences Contactless check-in/out Daily housekeeping Elevator unavailableElevator Laundry service Lockers Luggage storage Safety deposit boxes Smoking area ...
The difference in timing between the highest outdoor temperature and the highest heat flux transported indoors is known as the phase shift (ϕ) (Ballarini et al., 2020). A high phase shift (ϕ > 12 h) is indicative of high dynamic thermal performance in walls or roofs, whereas low ...
No energy loss, ageing or temperature effects have been modelled. Only excess PV energy can be sold to the grid. In other words, one cannot sell PV energy to the grid for a high price and at the same time buy cheaper electricity from the grid to cover the building’s needs. Energy ...
Lin and others [24] developed an optimization control strategy for Heating, Ventilation and Air-Conditioning (HVAC) systems, including temperature control for fan coil units (FCUs). Their proposed strategy includes dynamically setting the FCU temperature according to changes in outdoor temperature; ...
temperature when the WH is turned on is represented by 𝛼𝑊𝐻αWH, while the cooling effect on water temperature when the WH is turned off is represented by 𝛾𝑊𝐻γWH, which captures the thermal leakage due to the difference between the outside temperature and water temperature. La...
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Lin and others [24] developed an optimization control strategy for Heating, Ventilation and Air-Conditioning (HVAC) systems, including temperature control for fan coil units (FCUs). Their proposed strategy includes dynamically setting the FCU temperature according to changes in outdoor temperature; ...
(NP) hard problem, which can lead to an exponential increase in solution time for small problem cases. Besides, it does not utilize energy trading factors, such as real-time price, times of the day, outdoor temperature, buyers’ interest (BI), and storage capacity. In the same year, ...
Weather data comprising outdoor temperature, humidity and wind speed are merged as exogenous input with the consumption data. Further data preprocessing comprised data cleansing, normalization, and structure change. As machine learning and LSTM-RNN models are sensitive to the scale of the inputs, ...