The use of algorithms for light control

Because light directly influences human health, requirements on lighting levels must be constantly fulfilled. The lighting control market represents, therefore, an innovative sector that empowers a start-up's potential and where customers are highly looking for solutions that have low costs and high life expectancy, and that are least intrusive. Consequently, the control algorithms implemented are crucial in order to maximize efficiency while satisfying all the aforementioned specifications. This chapter introduces light-control algorithms as enabler of differentiation, which is a key requirement for a successful start-up rollout. Moreover, the proposed control lighting systems are customized and implemented in three real operational environments: two hospitals and one office building, all located in the Mediterranean area. The implementations show significant energy savings with low up-front and installation costs: this demonstrates the importance of control algorithms in lighting systems as high energy savings are achieved and lighting requirements fulfilled.

[1]  Anastasios I. Dounis,et al.  Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques , 2011 .

[2]  Jozef Hraska.  Chronobiological aspects of green buildings daylighting , 2015 .

[3]  Mohamed Boubekri.  Daylighting, Architecture and Health: Building Design Strategies , 2008 .

[4]  V I George,et al.  Integrated design and real-time implementation of an adaptive, predictive light controller , 2012 .

[5]  C. Balaras,et al.  Energy characteristics and savings potential in office buildings , 1994 .

[6]  D. Kolokotsa,et al.  Predictive control techniques for energy and indoor environmental quality management in buildings , 2009 .

[7]  Alice M. Agogino,et al.  Control of wireless-networked lighting in open-plan offices , 2011 .

[8]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[9]  Kostas Kalaitzakis,et al.  Adaptive lighting controllers using smart sensors , 2016 .

[10]  Ian Paul Knight.  Measured energy savings due to photocell control of individual luminaires , 1998 .

[11]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Theocharis Tsoutsos,et al.  Energy conservation techniques for hospital buildings , 2012 .

[13]  Sai On Cheung,et al.  An analysis of electricity end-use in air-conditioned office buildings in Hong Kong , 2003 .

[14]  Gilles Fraisse,et al.  Influence of the coupling between daylight and artificial lighting on thermal loads in office buildings , 2004 .

[15]  Ciji Pearl Kurian,et al.  An adaptive neuro-fuzzy model for the prediction and control of light in integrated lighting schemes , 2005 .

[16]  M. A. Alghoul,et al.  Research and development on aspects of daylighting fundamentals , 2013 .

[17]  T. S. B. Sudarshan,et al.  Intelligent Lighting System Using Wireless Sensor Networks , 2010, Ubicomp 2010.