The lighting industry is still a linear economy, despite the extensive use of light-emitting diode (LED) and the ban of incandescent/halogen lamps, claiming to be greener and more human centered. Light-emitting diode has changed radically the whole lighting industry with an increased luminaire efficacy more than four times higher compared with fluorescent lamps and their new opportunities for modern control systems. In the years to come, millions of fluorescent luminaires will become waste and will be replaced by LED luminaires. According to the Cost European Cooperation in Science & Technology Program the next step will be from sustainability to regenerative (enabling social and ecological systems to maintain a healthy state and to evolve) and to get there, circular economy is essential. In order to reduce even further the carbon footprint, the retrofit of existing luminaires and additional modern control systems should be the solution. Circular economy aspects for the lighting area were identified using the university adopted lighting retrofit solutions as case studies. For an LED retrofitted recessed luminaire 4 × T8 18 W studies showed a major installed power reduction, a good lighting distribution, but also revealed some problems: the retrofit luminaire has no certification, necessity of qualified personnel, high labor costs, etc. A major issue is the fact that luminaire design did not take into consideration circular economy aspects like the possibility of future retrofit solutions. It is important that from now on a different approach should be foreseen for the LED luminaire design. There is a section about vision, which plays an important role in preparing new luminaire generations with circular economy in mind.

For the next few years millions of fluorescent luminaires will become waste and will be replaced by light-emitting diodes—LED luminaires. According to the Cost European Cooperation in Science & Technology Program, the next step will be shifting from sustainability to regeneration (enabling social and ecological systems to maintain a healthy state and to evolve); to reach this point, a is circular economy becomes necessary. The Technical University of Cluj-Napoca’s Lighting Engineering Laboratory—LEL—is the main lighting independent consultant in Transylvania (the north-west region of Romania). For the Building Services Faculty, LEL adopted different energy efficient lighting solutions to replace existing fluorescent T8 luminaires. The best available techniques were evaluated over the last few years. Out of a range of different smart lighting control systems, LED were chosen and used for retrofitting the existing T8 luminaires or simply replacing them with new dedicated LED luminaires. The study analyzed five different lighting setups for upgrading the existing fluorescent T8 2*36W luminaires. One setup used T5 lamps and the others used retrofitted or dedicated LED lighting solutions. First the lighting quantities of each setup were evaluated under real and experimental conditions. Second, a programable power source was used for measuring power quality indicators corelated with the provided lighting outputs, under different voltage values and waveforms. For each lighting setup, an even and odd current harmonic limit check was performed in line with class C—lighting equipment, IEC 61000-3-2 requirements. A new energy efficiency and power quality indicator was proposed—the lighting apparent power density [VA/sq.m/100lx].

Detecting and locating faults in electrical cables has been a permanent concern regarding electrical power distribution systems. Over time, several techniques have been developed aiming to manage these faulty situations in an efficient way. These techniques must be fast, accurate, but, above all, efficient. This paper develops a new approach for detecting, locating, classifying, and predicting faults, particularly in different types of short-circuits in electrical cables, based on a robust artificial neural network technique. The novelty of this approach lies in the ability of the method to predict fault’s location and type. The proposed method uses the Matlab and Simulink platform and comprises four consecutive stages. The first one is devoted to the development of the Simulink model. The second one implies a large number of simulations in order to generate the necessary dataset for training and testing the artificial neural network model (ANN). The following stage uses the ANN to classify the location and the type of potential faults. Finally, the fourth stage consists of predicting the location and the type of future faults. In order to reduce the time and the resources of the simulation process, a virtual machine is used. The study reveals the efficiency of the method, and its ability to successfully predict faults in real-world electrical power systems.