Energy-Efficient Lighting in Educational Buildings: Audit and Illuminance Prediction with Artificial Neural Networks

In this study, the accuracy of indoor lighting simulations using DIALux software was evaluated by comparing simulated illuminance with field measurements taken at a 1 × 1 meter resolution across a structured grid. This spatial alignment enabled a robust statistical comparison, revealing that both datasets followed an approximately normal distribution, although simulated values exhibited higher variability and a slight tendency to overestimate illuminance in central zones. Descriptive statistics showed that the mean measured illuminance was 488.91 lux (SD = 106.58), while the simulated mean was 487.31 lux (SD = 129.06). A paired t-test indicated a statistically significant difference (p < .05), yet the high coefficient of determination (R2 = 0.92) confirmed a strong correlation between measured and simulated values. Visual analysis using heatmaps further supported this agreement, with both datasets showing higher illuminance near the center of the space and lower values at the edges. Despite this, both datasets failed to meet the EN 12,464-1 uniformity requirement (min/avg >0.6), suggesting uneven light distribution. To improve lighting performance and reduce energy consumption, alternative luminaire layouts were simulated, achieving an 18% reduction in energy use and improved uniformity (≈0.7). Optimal desk placement zones were also identified by analyzing consistent compliance with lighting standards under various lighting scenarios. Finally, artificial neural networks (ANNs), including Multilayer Perceptron (MLP) and Radial Basis Function (RBF) models, were used to predict illuminance based on spatial coordinates. The RBF network outperformed MLP with lower RMSE (17.61 lux) and higher R2 (0.9727), demonstrating superior ability to capture non-linear spatial variations. These findings validate the applicability of DIALux for early-stage lighting design and highlight the potential of ANN-based models for real-time lighting control and optimization in workspaces. The integration of simulation, measurement, and AI techniques offers a comprehensive approach to energy-efficient and user-centered lighting design.