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Every fifth inhabitant of our planet has no access to electric lighting. Most of them are poor people living in remote areas of developing countries. Recent progress in solid-state lighting technologies offers good opportunities to develop, commercialize and introduce off-grid lighting systems based on application of white light emitting diodes (WLEDs) in combination with photovoltaic solar panels, wind generators or tiny hydro power plants. Though strongly dependent on the mainstream progress in implementation of LEDs for general lighting, application of this technology in developing world has specific challengers, difficulties and even advantages. Lighting technology the developing world is up to leapfrog from splinters and kerosene wick lamps directly to LED lamps leaving incandescent and fluorescence lamps behind. Achievements and problems, history and future of implementation of solid-state lighting in remote villages of developing counties are discussed in this chapter.

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants’ location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants’ positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control.

Lighting technologies are transitioning into their next-generation phase through the “Internet of Things” (IoT) wave. We are observing the integration of many functions, including a controlling function to gain energy efficiency. This occurs through a convergence of technology that increases the intelligence of the lighting systems as well. Also, this convergence trend shows the effort to increase not only the intelligence, but also the connectedness of the lighting system. We present case studies that monitor the technology convergence that leverage patent analysis from different perspectives. The first case study monitors the connectedness of specific smart components of lighting systems. In this case study, we tried to monitor the convergence of lighting control strategies that yielded energy efficiency from 2009 to 2013. In the second case, we tried to monitor the convergence of technologies and their strength with the ones that yield energy efficiency in street lights. The third case study focuses on identifying energy efficiency technologies specifically used for working spaces. This case monitors the convergence of technologies with lighting technologies specified for working spaces. The methodologies that are used to study the convergence in the cases are International Patent Classification (IPC) co-classification in conjunction with cross-impact analysis, citation analysis, and our new approach to conduct trend impact analysis. In our new approach, we perform a trend impact analysis over a short period of time and then use a scenario analysis to anticipate the impact of the trend beyond the analysis period. Scenarios utilize detailed information, including the rate of change of co-classified patents, the number of patents in each class, and the convergence strength between two classes over time to anticipate the convergence trend. In our approach, the convergence strength rate of change is considered alongside two other parameters to understand the reason for the rise or decline in convergence strength, and possibility of its change in the future. This new approach is used in the first case study. Co-classification analysis in conjunction with impact analysis is used for the second case study. The methodologies used in the third case study are the same as second case along with citation analysis.