Designing metal halide-based road illumination systems in developing countries using regression and neural networksThe dataset is available through this link - Sourin Bhattacharya; Khondekar Lutful Hassan; Pallav Dutta (Results in Engineering)
Public road illumination systems shape the urban landscape and are ineluctable to ensuring the nocturnal safety of motorists, wayfarers, and pedestrians. In developing nations including India, various local bodies still rely on time-honoured discharge lamps including metal halide (MH) lamps for road lighting. This study aimed to develop practicable models for predicting luminance and energy efficiency parameters of MH-based public road illumination systems. Photometric simulations were conducted for 80,000 installation combinations considering single-sided arrangements of MH luminaires and candidate models of predicting luminance and energy efficiency parameters were propounded with multiple regression analysis and two-layer feed-forward artificial neural networks (ANNs). Luminaire power per unit road length (τ, W/km) and standard specular factor (S1) were found to be the most important predictor variables for the prediction of photometric parameters and installation energy efficiency respectively. For some conventional design configurations, the performances of the models were assessed and the 50-neuron ANN model performed well with an error margin of – 1.45 % to + 1.71 % for the prediction of average luminance, of – 8.67 % to + 9.08 % for the prediction of overall uniformity, of – 8.31 % to + 16.40 % for the prediction of longitudinal uniformity and of – 1.56 % to + 1.52 % for the prediction of installation energy efficiency. Through extensive photometric simulation and data analysis, this study provides useful insights for the commissioning of road lighting projects especially pertaining to the usefulness of ANN models for the planning and optimization of public road illumination systems in developing countries.