Multiobjective energy efficient street lighting framework: A data analysis approach

A data analysis approach for designing an energy efficient street lighting framework is proposed to maximize both energy efficiency and uniformity of the system. A multiobjective optimization problem on obtaining energy efficiency is formulated in a comprehensive manner. Three multiobjective evolutionary optimization algorithms such as nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2 and multiobjective differential evolutionary algorithm are used to analyse the approximated Pareto solutions of our proposed model. The performance of considered algorithms are presented and compared with regard to different metrics. The results from the best algorithm, in terms of convergence and diversity, among the algorithms are then validated using DIALux to ensure the recommendation for the standardization in different aspects. The proposed work contributes a comprehensive data analysis on genetic algorithm solutions towards obtaining a multiobjective energy efficient street lighting which is beyond the scope of the existing works. The results obtained by the proposed method are also compared with existing DIALux results. The improvement of energy efficiency obtained by the proposed methodology over existing works is shown in terms of various aspects.