Soft senzor za predviđanje količine sunčeve svetlosti u prostoriji
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Aug 8, 2017
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Apstrakt
U ovom radu je predstavljen razvoj soft senzora za predikciju količine sunčeve svetlosti u prostoriji. Procena je zasnovana na matematičkom modelu koji koristi veliki broj promenljivih među kojima su geografska širina i dužina, dimenzije prostorije, orijentacija i dimenzije prozora, dan u godini i trenutno vreme. Osvetljenost radne površine je procenjena za prostoriju sa jednim prozorom koji može biti orijentisan prema bilo kojoj strani sveta. Kako bi se ostvarile uštede i željeni komfor, vrednosti dobijene pomoću ovog soft senzora bi se mogle iskoristi kao merenja za upravljanje sistemom veštačkog osvetljenja.
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Kako citirati
ČONGRADAC, Velimir; LAZAREVIĆ, Slađana; BOJANIĆ, Dubravka.
Soft senzor za predviđanje količine sunčeve svetlosti u prostoriji.
Zbornik Međunarodnog kongresa i izložbe o KGH, [S.l.], v. 46, n. 1, p. 350-356, aug. 2017.
Dostupno na: <https://izdanja.smeits.rs/index.php/kghk/article/view/2807>. Datum pristupa: 16 oct. 2026
Sekcija
Prilaz pametnim održivim i otpornim kućama, naseljima i gradovima
Reference
[1] Wen, Y.J., Agogino, A.M., Personalized dynamic design of networked lighting for energy-efficiency in open-plan offices, Energy and Buildings, 2011, Vol. 43, 1919-1924
[2] Athienitis, K.A, Tzempelikos, A., A methodology for simulation of daylight room illuminance distribution and light dimming for a room with a controlled shading device, Solar Energy, 2002, Vol. 72, 271-281
[3] Yang, I.H., Nam, E.J., Economic analysis of the daylight-linked lighting control system in office nbuildings, Solar Energy, 2010, Vol. 84, 1513-1525
[4] Čongradac, V., Prica, M., Paspalj, M., Bojanić, D., Čapko, D., Algorithm for blinds control based on the optimization of blind tilt angle using a genetic algorithm and fuzzy logic, Solar Energy, 2012, Vol. 86, 2762-2770
[5] Grynning, S., Time, B., Matusiak, B., Solar shading control strategies in cold climates – Heating, cooling demand and daylight availability in office spaces, Solar Energy, 2014, Vol. 107, 182-194
[6] Aparna, D., Saikat, K.P., Artificial illumination during daytime in residential buildings: Factors, energy implications and future predictions, Applied Energy, 2015, Vol. 158, 65-85
[7] Fasi, M.A., Budaiwi, I.M., Energy performance of windows in office buildings considering daylight integration and visual comfort in hot climates, Energy and Buildings, 2015, Vol. 108, 307-316
[8] Solar geometry variables, Energy and Environment Management, College of Environmental Design, Berkley
[9] Al-Rawahi, N.Z., Zurigat, Y.H, Al-Azri, N.A., Prediction of Hourly Solar Radiation on Horizontal and Inclined Surfaces for Muscat/Oman, The Journal of Engineering Research, 2011, Vol. 8, 19-31
[10] Katiyar, A.K., Kumar, A., Pandey, C.K., Katiyar, V.K., Abdi, S,H., Correlations for the estimation of monthly mean hourly diffuse solar radiation: a time dependent approach, International Journal of Energy and Environment, 2010, Vol. 1, 833-840
[11] Sudan, M., Tiwari, G.N., Al-Helal, I.M., A daylight factor model under clear sky conditions for building: An experimental validation, Solar Energy, 2015, Vol. 2015, 379-389
[12] De Rosa, A., Ferraro, V., Kaliakatsos, D., Marinelli, V., Calculating indoor natural illuminance in overcast sky conditions, Applied Energy, 2010, Vol. 87, 806-813
[13] Boyce, P., Raynham, P., The SLL Lighting Handbook, CIBSE, London, United Kingdom, 2009
[2] Athienitis, K.A, Tzempelikos, A., A methodology for simulation of daylight room illuminance distribution and light dimming for a room with a controlled shading device, Solar Energy, 2002, Vol. 72, 271-281
[3] Yang, I.H., Nam, E.J., Economic analysis of the daylight-linked lighting control system in office nbuildings, Solar Energy, 2010, Vol. 84, 1513-1525
[4] Čongradac, V., Prica, M., Paspalj, M., Bojanić, D., Čapko, D., Algorithm for blinds control based on the optimization of blind tilt angle using a genetic algorithm and fuzzy logic, Solar Energy, 2012, Vol. 86, 2762-2770
[5] Grynning, S., Time, B., Matusiak, B., Solar shading control strategies in cold climates – Heating, cooling demand and daylight availability in office spaces, Solar Energy, 2014, Vol. 107, 182-194
[6] Aparna, D., Saikat, K.P., Artificial illumination during daytime in residential buildings: Factors, energy implications and future predictions, Applied Energy, 2015, Vol. 158, 65-85
[7] Fasi, M.A., Budaiwi, I.M., Energy performance of windows in office buildings considering daylight integration and visual comfort in hot climates, Energy and Buildings, 2015, Vol. 108, 307-316
[8] Solar geometry variables, Energy and Environment Management, College of Environmental Design, Berkley
[9] Al-Rawahi, N.Z., Zurigat, Y.H, Al-Azri, N.A., Prediction of Hourly Solar Radiation on Horizontal and Inclined Surfaces for Muscat/Oman, The Journal of Engineering Research, 2011, Vol. 8, 19-31
[10] Katiyar, A.K., Kumar, A., Pandey, C.K., Katiyar, V.K., Abdi, S,H., Correlations for the estimation of monthly mean hourly diffuse solar radiation: a time dependent approach, International Journal of Energy and Environment, 2010, Vol. 1, 833-840
[11] Sudan, M., Tiwari, G.N., Al-Helal, I.M., A daylight factor model under clear sky conditions for building: An experimental validation, Solar Energy, 2015, Vol. 2015, 379-389
[12] De Rosa, A., Ferraro, V., Kaliakatsos, D., Marinelli, V., Calculating indoor natural illuminance in overcast sky conditions, Applied Energy, 2010, Vol. 87, 806-813
[13] Boyce, P., Raynham, P., The SLL Lighting Handbook, CIBSE, London, United Kingdom, 2009