Accurately predicting indoor illuminance from daylight during the early stages of building design is an important factor in saving energy and the costs associated with lighting. The objective of this study was to predict sky luminance distribution using the Commission Internationale de l’éclairage (CIE) standard sky model, and propose a method that can be used to predict indoor illuminance. Results obtained from the proposed prediction method were compared and verified with simulation values obtained by Desktop Radiance. From the CIE overcast sky, the zenith/horizon ratio was 3:1. From the CIE clear sky, the luminance value was highest around the sun. In contrast, the luminance value was lowest in the opposite direction of the sun when the angle between the sun and sky elements was 90°. In addition, this study suggested an indoor illuminance prediction method by applying the effects of sky luminance, direct sunlight, and wall reflection elements. When the proposed equation’s calculation results were compared with Desktop Radiance simulation’s value in overcast and clear sky, all statistically analysis (R², MBE, Cv(RMSE), t-value, p-value) satisfied each standard and showed high correlations. Consequently, it was established that the predicted indoor illuminance obtained from the proposed prediction method was accurate and can be used to predict the level of indoor illuminance. The results further revealed that it is possible to calculate indoor illuminance when installing blinds, by substituting variable values of visible light transmittance (VLT).

In this study, we address the algorithm of the calculation formula from sky luminance distribution to vertical illuminance and indoor illuminance. To predict daylight, studies about sky luminance distributions and the DeLight daylight model were investigated. This paper compared three well-known sky distribution models (Perez model, Igawa model, and CIE standard sky model) with measured data. The charge coupled device (CCD) camera was used as a measurement method for sky luminance distribution. Indoor illuminance values calculated with those well-known sky distribution models are compared with measured indoor illuminance data. The following conclusions were obtained: (1) In the results of R², mean bias error (MBE), and Cv(RMSE) analysis, the CIE standard sky model showed the lowest error rate with measured data. Between the Perez model and Igawa model, the Igawa model showed a lower error rate; (2) When we compared the sky classification of the Perez model and the Igawa model, both models classified the sky similarly to the CIE standard sky model in March. However, the classification of the sky in the CIE standard sky model, the Perez model, and the Igawa model differed in some of the July data because of high solar elevation; (3) The illuminance of the center point of the room was calculated using the well-known sky luminance distribution model, the Igawa model has a lower error rate than the Perez model in Korea.

This study aims to suggest a basis for the selection of technologies for developing high-performance buildings to reduce energy consumption and greenhouse gas emissions. Energy-saving technologies comprising 15 cases were categorized into passive, active, and renewable energy systems. EnergyPlus v8.8.0 was used to analyze the contribution of each technology in reducing the primary energy consumptions and CO₂ emissions in the Korean climate. The primary energy consumptions of the base model were 464.1 and 485.1 kWh/m²a in the Incheon and Jeju region, respectively, and the CO₂ emissions were 83.4 and 87.4 kgCO₂/m²a, respectively. Each technology (cases 1–15) provided different energy-saving contributions in the Korean climate depending on their characteristics. The heating, cooling, and other energy-saving contributions of each technology indicate that their saving rates can be used when selecting suitable technologies during the cooling and heating seasons. Case 15 (active chilled beam with dedicated outdoor air system + ground source heat pump) showed the highest energy saving rate. In case 15, the Incheon and Jeju models were reduced by 189.4 (59.2%) and 206.2 kWh/m²a (57.4%) compared to the base case, respectively, and the CO₂ emissions were reduced by up to 32.7 (60.8%) and 35.6 kgCO₂/m²a (59.3%), respectively.

To provide useful information concerning energy-conserving heating, ventilation, and air-conditioning (HVAC) systems, this study used EnergyPlus to analyze in detail their operational characteristics and energy performance. This study also aimed to understand the features of the systems under consideration by investigating the dry-bulb temperature, relative humidity, and airflow rate at major nodes in each system’s schematic. Furthermore, we analyzed the indoor environment created by each HVAC system, as well as examining the cooling energy consumptions and CO₂ emissions. The HVAC systems selected for this study are the variable air volume (VAV) commonly used in office buildings (base-case model), constant air volume (CAV), under-floor air distribution (UFAD), and active chilled beam (ACB) with dedicated outdoor air system (DOAS). For the same indoor set-point temperature, the CAV’s supply airflow was the highest, and VAV and UFAD were operated by varying the airflow rate according to the change of the space thermal load. ACB with DOAS was analyzed as being able to perform air conditioning only with the supply airflow constantly fixed at a minimum outdoor air volume. The primary cooling energy was increased by about 23.3% by applying CAV, compared to VAV. When using the UFAD and ACB with DOAS, cooling energy was reduced by 11.3% and 23.1% compared with VAV, respectively.

This study aims to suggest a basis for the selection of technologies for developing high-performance buildings to reduce energy consumptions and greenhouse gas emissions. Energy-saving technologies comprising of 15 cases were categorized into passive, active, and renewable energy systems. EnergyPlus v8.8 was used to analyze the contribution of each technology in reducing the primary energy consumptions and CO2 emissions in the Korean climate. The primary energy consumptions of the base model were 464.1 and 485.1 kWh/m²a in the Incheon and Jeju, respectively, and the CO2 emissions were 83.4 and 87.4 kgCO2/m²a, respectively. Each technology (cases 1–15) provided different energy-saving contributions in the Korean climate depending on their characteristics. The heating, cooling, and other energy-saving contributions of each technology indicate that their saving rates can be used when selecting suitable technologies during the cooling and heating seasons. Case 15 (active chilled beam with dedicated outdoor air system + ground source heat pump) showed the highest energy saving rate. In case 15, the Incheon and Jeju models were reduced by 189.4 (59.2%) and 206.2 kWh/m²a (57.4%) compared to the base case, respectively, and the CO2 emissions were reduced by up to 32.7 (60.8%) and 35.6 kgCO2/m²a (59.3%), respectively.