Sky type classification in Harbin during winter

Zi, Ying; Sun, Chuang; Han, Yunsong

doi:10.1080/13467581.2020.1752217

Cited by 3 publications

(3 citation statements)

References 24 publications

(26 reference statements)

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“…Strictly speaking, there is unavailability of Daylight illuminance data generation daylight data in many areas (Dieste-Velasco et al, 2020). For example, sky luminance measurements have been carried out only in a few areas in Asia (Zi et al, 2020). Such locations include Singapore (Wittkopf and Soon, 2007), China (Luo et al, 2015), Hong Kong (Li et al, 2004), Thailand (Janjai and Plaon, 2011) and Saudi Arabia (Alshaibani et al, 2020).…”

Section: Review On Energy Efficiency Daylight Illuminance and Luminou...mentioning

confidence: 99%

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Application of luminous efficacies for daylight illuminance data generation in subtropical Hong Kong

Aghimien

2022

SASBE

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PurposeDaylight plays a crucial role in the attainment of building energy savings. Harnessing daylight in building designs will require the need for daylight illuminance data. However, daylight illuminance data are scarce due to few measuring stations. Aside from being sparse, illuminance measuring stations can be expensive to set up, thus making the luminous efficacy model a better alternative. Hence, this study attempted to model horizontal luminous efficacies under the 15 Commission internationale de l'éclairage (CIE) standard skies. Therefrom, daylight illuminance was estimated from a proposed vertical luminous efficacy model.Design/methodology/approachMeasured solar irradiance, daylight illuminance and luminance distribution data were gathered from the local measuring station in Hong Kong. The luminance distribution data were used to classify the skies into the 15 CIE standard skies. Next, the solar irradiance and daylight data were used to derive the horizontal luminous efficacies under each standard sky. Furthermore, a vertical luminous efficacy model developed using the measured data was described, and this was used to predict vertical illuminance.FindingsIt was observed that Skies 1, 8 and 13 seem to be predominant in Hong Kong. Also, the result showed that constant luminous efficacies could be used for deriving illuminance data. Furthermore, horizontal luminous efficacy ranged from 40 to 190lm/W, indicating that daylight can provide sufficient visibility during working hours. The vertical luminous efficacy model proves to offer reasonable estimations of vertical illuminance data.Research limitations/implicationsFurther work needs to be done with more measured data to cover for spring seasons. The described model still needs to be fitted with different world climates to ascertain its universal applicability. The evaluations need to be done under obstructed sky conditions to cater for dense and clustered urban centres.Practical implicationsThe discussed luminous efficacy model could be used to derive illuminance data in the absence of measured daylight illuminance data, especially in the subtropical region. Also, the comparative advantage of daylight over artificial lighting was highlighted in this study.Originality/valueUnlike previous studies, this paper discusses the luminous efficacies of global, direct and diffuse components under the 15 CIE standard skies. Furthermore, the described luminous efficacy analysis provides an approach for deriving vertical and horizontal illuminance data. Such vertical data will be required for analysing building lighting requirements, sensible heat from electric lighting, and energy savings from daylighting controls. Also, the information on horizontal luminous efficacies will help evaluate solar roof and skylight designs.

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Section: Review On Energy Efficiency Daylight Illuminance and Luminou...mentioning

confidence: 99%

“…, 2020). For example, sky luminance measurements have been carried out only in a few areas in Asia (Zi et al. , 2020).…”

Section: Review On Energy Efficiency Daylight Illuminance and Luminou...mentioning

confidence: 99%

Application of luminous efficacies for daylight illuminance data generation in subtropical Hong Kong

Aghimien

2022

SASBE

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“…The sky scanner is the standard instrument for measuring sky luminance distribution . Despite the high interest in those measurements, very few studies at only a handful of European Torres et al, 2010a, b; and Asian Zi et al, 2020) locations have been conducted to characterize the sky under the CIE standard, mainly due to the scarcity of sky scanner devices available to gather sky luminance data.…”

Section: Introductionmentioning

confidence: 99%

Daylight modeling for energy efficiency and visual comfort in buildings

Granados López

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Searching and selecting an adequate methodology for daylight modeling is essential in the design of energy efficient buildings that guarantee the visual, physical and psychological comfort of their occupants. The first step in determining the indoor building illuminance lies in knowing the outdoor illuminance. This dissertation addresses this key aspect through different strategies such as luminous efficacy models and the determination of the angular distribution of the sky's luminance. Daylight is strongly determined by sky conditions. The CIE/ISO standard provides a good general framework to represent the real conditions of the sky, covering the entire probable spectrum of skies, and has been used as a reference throughout this work. The characterization of the skies according to the CIE standard requires experimental measurements of the luminance distribution of the sky, scarcely recorded in terrestrial meteorological facilities. The thesis proposes, as alternatives for the classification of skies according to the CIE taxonomy, the use of meteorological indices, sky images and algorithms based on artificial intelligence. The structure and efficiency of the machine learning algorithms used, both neural networks and decision trees, have been optimized through feature selection procedures in the case of the use of meteorological indices and through image pre-processing techniques, as a step prior to using the classification algorithm. The thesis has also developed a new locally calibrated luminous efficacy model, with excellent results both when used for all-sky types and for clear, overcast and partially overcast sky conditions.

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A comparative study of machine learning methods for identifying the 15 CIE standard skies

Aghimien,

Li,

Tsang

et al. 2024

Journal of Building Physics

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For energy-efficient building designs, the solar irradiance and daylight illuminance derived from the CIE standard skies are useful. Over time, the sky luminance distributions have been used to identify these standard skies, but these are sparingly measured. Thus, the use of available climatic variables has become a viable alternative. Nevertheless, it is necessary to determine if these climatic variables could correctly identify these skies. This study addresses the lack of luminance distribution measurement by classifying the standard skies using measured climatic data in Hong Kong. The classification approach was improved by using the machine learning (ML) method. For comparative analysis, five popular ML classification algorithms i.e., decision tree (DT), k-nearest neigbhour (KNN), light gradient boosting machine (LGBM), random forest (RF) and support vector machines (SVM) were used. The findings show that accuracies of 68.1, 73.1, 74.3, 74.5, and 75.4% were obtained for the DT, KNN, SVM, LGBM, and RF models, respectively. Similarly, the F1 scores were 66.6, 70.2, 71.8, 72.1 and 72.9%, for the DT, KNN, SVM, LGBM, and RF models. The result shows that the RF model gave the best performance while DT performed the least. Also, the obtained accuracies and F1 scores show that all models would classify the standard skies with reasonable accuracy. Furthermore, feature importance was done, and it was found that Kd, Tv, Kt, α, sun, and cld are the most important input parameters for sky classification. Lastly, vertical solar irradiance ( GVT) and illuminance ( GVL) were estimated using the skies predicted by the proposed models. Upon predictions, it was observed that the GVT ranged from 14.7 to 24.6% while the GVL from 13.8 to 19.9%. Generally, most of the predictions were less than 20%, which shows good predictions were obtained from the models.

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Sky type classification in Harbin during winter

Cited by 3 publications

References 24 publications

Application of luminous efficacies for daylight illuminance data generation in subtropical Hong Kong

Application of luminous efficacies for daylight illuminance data generation in subtropical Hong Kong

Daylight modeling for energy efficiency and visual comfort in buildings

A comparative study of machine learning methods for identifying the 15 CIE standard skies

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