Energyefficiency strategies based on daylight-artificial light integrated schemes have proved to be efficient by many researchers worldwide. But much larger energy savings with the benefit of visual and thermal comfort can be achieved when systems integration strategies are competently designed. They require a high level of expertise and familiarity with new design techniques. This study describes the results of three computational models suitable for the optimum integration of visual comfort, thermal comfort, and energy consumption in schemes where daylight and artificial light are integrated. This mainly involves: (i) a system identification approach in lighting control strategy, (ii) a fuzzy logic based controller to reduce glare, increase uniformity and thermal comfort, and (iii) an adaptive predictive control scheme for the dimming of artificial light. In addition to the above models the scheme must take account of occupancy and user wishes. The anticipated synergetic effects of the computational models have been validated using climate data. A SIMULINK environment is established for the real time control and analysis of daylight-artificial light integrated schemes. Overall, the schemes maximise energy cost saving while optimizing the performance and the quality of the visual environment.
Advanced lighting simulation tools as well as computationally intelligent systems present the possibility of using a model based on computation as a means of controlling lighting on the visual task. Lighting control has now become an essential element of good design and an integral part of energy management programmes. This paper presents a novel computational model suitable for the adaptive predictive control of artificial light in accordance with the variation of daylight. Simulated data and an adaptive neuro-fuzzy inference system are incorporated into the model. The software package Radiance is used to carry out the simulation. In this process, the role of a simulator is considered as the source of the system knowledge by which a supervised learner, implemented in adaptive neuro-fuzzy inference system is trained for faster predictions. The goal of this paper is to make use of the benefits of the hybridization between simulation and machine learning for the purpose of light control.
Availability of quality power has become an important issue for industrial utilities due to frequent performance variations in process industries. Increase in the generating capacity has not kept up pace of power demand, which results into shortage of power supply and power system network is normally subjected to varying and unequal loads across the three phases. Continuous variation of single-phase loads on the power system network leads to voltage variation and unbalance, most importantly; the three-phase voltages tend to become asymmetrical in nature. Application of asymmetrical voltages to induction motor driven systems severely affects its working performance. This paper presents the effects of voltage variation and unbalance on the performance of an induction motor driven centrifugal pump with a case study.
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