Open Access
Review
Table 1
Summary of the related works based on the data-driven model.
| Work source | Key findings | Performance |
|---|---|---|
| Mavromatidis et al. (2014) | For the estimation of the daylight factor, a polynomial-based regression model was proposed. | Prediction of daylight factor with root mean square levels 0.99 by a set of independent variables |
| Borile et al. (2017) | Light sensors on luminaires and a workplace reference point were used to collect experimental data. | Adjustable dimming levels; Minimized energy use |
| Le et al. (2014) | Developed using a Support Vector Machine (SVM)-based Machine Learning (ML) method. | Thermal discomfort reduction through blind control with SVM technique |
| Caicedo and Pandharipande (2016) | Support Vector Regression (SVR)-based energy consumption model | Energy savings |
| Xiong and Tzempelikos (2016) | Model-based control (MBC) techniques for lighting and shading controls were developed; Minimum sensor inputs (irradiation, indoor/outdoor illuminance) were considered. | Satisfies the visual comfort criteria; Maximize daylight utilization, Minimize lighting energy use. DGP values remained below 0.35; lighting energy use was reduced |
| Sadeghi et al. (2017) | Predict electric light dimming and roller shade lowering/raising actions. The intermediate operational states of electric lighting and shading systems were predicted with Bayesian regression models. | Data on environmental parameters, individual characteristics and human attributes governing human-shading and – electric lighting interactions |
| Gunay et al. (2017) | “Discrete-time Markov logistic regression models from the light-on and blind-closing behaviours in recursion” were developed. | Modest electricity energy reductions of 0.9 kWh/m2 only with adaptive lighting controls and 1.2 kWh/m2 with both adaptive lighting and blind controls |
| Sanjeev Kumar et al. (2020b) | Real-time data driven predictive models for thermal and visual comfort. Luminaire dimming and ensemble learning based window blind position prediction; Daylight glare assessment models using SVM, decision tree, ensemble tree, and ANN | The model consumes 17% less power than the uncontrolled system and 15% less power than the baseline system |
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