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The use of Prismatic Skylights and its effects as a passive Energy Conservation Strategy in “Residential” and “Big Box Commercial Buildings” in hot and humid climate has been evaluated throughout this project. The potential benefits of using skylights reside in the fact that it reduces electrical lighting necessities but at the same time it contributes to an upsurge of the Cooling Loads of the conditioned space. Acknowledging the impact of skylights is fundamental to elaborate an optimized design of a building’s energy efficient mechanical system. To reach a sound conclusion, the evaluated buildings were modeled and their performance was simulated using the Department of Energy Simulation Program “Energy Plus”. To be able to compare the Energy Conservation Measure case (Using Skylights) with the Base Line (No Skylights), a photometric sensor was modeled to ensure that both cases sourced the same amount of light visible in the electromagnetic spectrum. Considering the Heating, Cooling and lighting energy consumption as variables, the variance between the ECM and the Base line for the residential case was 5% more energy consumption with skylights. For the Big Box Commercial Building there was a 5% deduction in energy consumption in the ECM case using 5% roof area covered with skylights. The results obtained from this investigation reveal a very promising effect in the implementation of skylights in “Big Box Commercial Buildings”, but not so optimistic in the case of “Residential Buildings” for hot and humid climate as shown by the simulation and monitoring data from the experimental case.

A trajectory records the evolution of the position of a moving object in a space during a time interval. In Spaccapietra's trajectory model, trajectories are segmented in subintervals called stops and moves. On the other hand, during some periods failures can occur in the transmission of data of the trajectory causing missings of information. In this paper, we extend Spaccapietra's model by incorporating the missing information as a component of a trajectory. We consider this issue not only with regard to the object position but also with regard to other attributes of the trajectory (complementary attributes). We propose a classification for these attributes, depending on whether they are constant or variable during the stops and the moves. We also propose two algorithms: i) to convert a sequence of observations of a trajectory into stops, moves and missings. ii) to check that the data recorded for the attributes whose value must be constant is consistent.