Enhancing Energy Efficiency of Buildings Using Smart Technologies and Automation in Lighting
Abstract
Lighting utilizes a large portion of the energy in commercial and academic buildings. One method of enhancing the energy efficiency of buildings is to implement smart technologies in lighting, such as occupancy sensors. Occupancy-based lighting controls can achieve energy savings, particularly in areas of low and intermittent occupancies, such as library stacks. However, the economic benefits of occupancy-controlled lighting are strongly affected by several parameters such as sensor density, the delay time setting of lighting control, type of lighting, percentage occupancy, etc. University library buildings, which are characterized by their unique occupancy and lighting usage patterns, merit specific focus and additional study. This research presents a long-term case study of occupancy patterns and lighting energy in a university library. A general analysis of the economic feasibility of occupancy-controlled lighting in a library is discussed, including best practices for the deployment of occupancy sensors to maximize energy savings.
One other method of enhancing the energy efficiency of buildings is to conduct lighting energy audits and implement recommended energy saving measures. Automating some elements of the energy assessments would augment the manual auditing process and eliminate simple, time-consuming tasks, and provide additional depth of analysis. This research presents an automated process of identifying the light type. In determining the light type, an optical spectrometer is used to measure the light intensities across the spectrum of wavelengths. The light types are classified using the closest match between the reference and measured optical spectra based on the CVRMS error values. The robustness of the classification algorithm was tested at 10 buildings of different types, functionalities, and ceiling heights. A total of 260 lamps were tested and the CVRMSE algorithm correctly classified the light type in 95% of the instances. In addition, the algorithm was also used able to identify the lamp type in presence of ambient light.
Citation
Shekhadar, Saurabh Nagesh (2021). Enhancing Energy Efficiency of Buildings Using Smart Technologies and Automation in Lighting. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /196398.