controllers have also been suggested. One type fuzzy controller for building control has been designed with a pre-determined cost function that incorporates the major factors of interest to the control designer with relative importance weightings . Simulations performed by the researchers have indicated that this fuzzy control system can be effective at tracking set points under variable external loads, but it does not incorporate individualized preferences or enable tailored controls. While the proposed system attempts to incorporate both HVAC and lighting systems, it is limited by its fully decentralized implementation. An adaptive neuro-fuzzy controller for lighting has also been suggested for an artificial lighting and automatic window blinds integrated controller that considers impacts to both lighting and HVAC systems though evaluation of both daylight and solar heat gain (Kurian et al., 2008). This system relies heavily on extensive training data sets, and although it is capable of considering both visual and thermal comfort in conjunction with energy savings, the tradeoffs between comfort and savings and relative weightings are not explicit. Genetic algorithms (GA) have also been suggested for in situ parameter updating for control decisions . The window blinds control scheme suggested by Guillemin et al. utilizes GA for nightly parameter updates. These updates are based on knowledge of manual overrides to the automated system, i.e. when window blind angle was set by the automated system but changed by the occupant. In this way the system adapts to the preferences of the occupants. This approach is useful for blind control because of the generally poor characterization of objective criteria for determining the preferred settings of blinds in daylit spaces with respect to known information including sun position and irradiance. This technique may have implications for other systems such as ventilation and temperature which have complex interactions with respect to comfort; however, with environmental conditions such as desktop illuminance where occupants are capable of explicitly specifying their preference, more reliable information can be gained by providing them with direct selection. As the algorithms for building control become more sophisticated, industry and research entities continue to develop the hardware and communication systems that implement algorithms. Web-based monitoring systems are of interest because
controllers have also been suggested. One type fuzzy controller for building control has been designed with a pre-determined cost function that incorporates the major factors of interest to the control designer with relative importance weightings . Simulations performed by the researchers have indicated that this fuzzy control system can be effective at tracking set points under variable external loads, but it does not incorporate individualized preferences or enable tailored controls. While the proposed system attempts to incorporate both HVAC and lighting systems, it is limited by its fully decentralized implementation. An adaptive neuro-fuzzy controller for lighting has also been suggested for an artificial lighting and automatic window blinds integrated controller that considers impacts to both lighting and HVAC systems though evaluation of both daylight and solar heat gain (Kurian et al., 2008). This system relies heavily on extensive training data sets, and although it is capable of considering both visual and thermal comfort in conjunction with energy savings, the tradeoffs between comfort and savings and relative weightings are not explicit. Genetic algorithms (GA) have also been suggested for in situ parameter updating for control decisions . The window blinds control scheme suggested by Guillemin et al. utilizes GA for nightly parameter updates. These updates are based on knowledge of manual overrides to the automated system, i.e. when window blind angle was set by the automated system but changed by the occupant. In this way the system adapts to the preferences of the occupants. This approach is useful for blind control because of the generally poor characterization of objective criteria for determining the preferred settings of blinds in daylit spaces with respect to known information including sun position and irradiance. This technique may have implications for other systems such as ventilation and temperature which have complex interactions with respect to comfort; however, with environmental conditions such as desktop illuminance where occupants are capable of explicitly specifying their preference, more reliable information can be gained by providing them with direct selection. As the algorithms for building control become more sophisticated, industry and research entities continue to develop the hardware and communication systems that implement algorithms. Web-based monitoring systems are of interest because
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