Open Access
Review
Table 2
Summary of the related works based on the prediction model for daylight artificial light integration.
| Literature | Data | Window blind control | Algorithm details | Model evaluation | Performance |
|---|---|---|---|---|---|
| Xie and Sawyer (2021) | Predictors: Irradiance, sky ratio, glare; Response: Window Blind Position; Data Source: Experimental real-time field data | Type: Venetian Blind; Position: East, West, North, and South; Control: Up–down, intermediate position with static slat angle of 45° | Algorithms: KNN, SVM, and RF; Parameter Tuning: Hyperparameter Optimization | Statistical: NA; Real-time: window venetian blind Slat angle control 0°, 15°, 30°, and 45° for glare prediction below 0°, 15°, and 30° | Could prevent 86.5%–96.9% of the glare and potentially reduce lighting energy use by 80.8% |
| Sanjeev Kumar et al. (2020ª) | Predictors: Indoor and outdoor window illuminance, work plane/ceiling illuminance, vertical illuminance; Response: Window Blind Position DGI, DGP, and GS; Field data collected from test room | Type: Venetian Blind; Position: East; Control: Up–down and intermediate position with static slat angle of 45°. | Algorithms: ET, ANN, GPR, and SVM; Parameter Tuning: Bayesian Hyperparameter Optimization; Feature Selection: DT; Type: Regression for DGP/DGI and classification for GS | Regression: MSE, RMSE, MAE, MAPE, and R2; Classification: Accuracy, PE, AUC-RoC, Precision, and Recall; Hypothesis: Friedman’s ranking test for model selection | ET model performs better than other models. The accuracy of DGP, DGI and GS are 99.84%, 99.39% and 94.4%; Total energy Consumes 17% less power than the uncontrolled system and 15% less power than the baseline system. |
| Chiesa et al. (2020) | Predictors: Illuminance (indoor/outdoor) and temperature Response: Blind Control, and LED; Data Source: Experimental (50 × 50 × 50 cm) Set up | Type: Venetian Blind; Position: East, and West; Control: Slat Angle Control (No up-down control) | Algorithms: Fuzzy Logic; Parameter Tuning: NA; Feature Selection: NA; Type: NA | Real-time: Indoor illuminance (300 lux), outdoor illuminance (1000 lux), and Lighting Energy. | When illumination is controlled by zones, Zones 1 and 2 consume power of 0.7 W and 0.69 W respectively compared to 4 W and 1 W without control |
| Yeon et al. (2019) | Predictors: Outdoor temperature, Relative Humidity, Solar Altitude, Solar Radiation, zone people occupant count; Response: Blind Slat angle (0 to 180°); | Type: Venetian Blind; Position: South; Control: Slat angle control (no up and down control) | Algorithms: ANN; Parameter Tuning: N/S; Feature Selection: N/S; Type: Regression | Statistical: RMSE; | The overall energy consumption was 9.1% lower than the baseline scenario of blind angle set at 50°. |
| Sadeghi et al. (2017) | Predictors: environmental variables, human attributes; Response: Blind up and down position; Data Source: Field study conducted in private offices | Type: motorized roller shades; Position: South; Control: Raising and lowering (25%, 50%, 75%, and 100%) | Algorithms: Bayesian modeling and BR models; Parameter Tuning: N/S; Feature Selection: N/S; Type: Regression | Statistical: RMSE | BMBCL models predicted shade raising/lowering and electric light dimming actions. BR models predicted the intermediate operating states of the systems. |
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