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.

Energy efficiency strategies based on daylight-artificial light integration have grown exponentially in recent years. Taking into account the dynamics to be considered for control and the dependence on natural and occupancy factors, it is better to use a test workbench prior to setting up the final control scheme. This work describes a climate model based test workbench for the real time testing of the control of luminaires and window blinds in a daylight-artificial light integrated scheme. The established climate model based control scheme suitable for the optimum integration of visual comfort, thermal comfort, and energy consumption can be tested for any ecological conditions. The input irradiance from a BF5 sensor, the internal temperature from a Micro DAQ logger, the occupancy and photo sensors associated with the luminaire all provide input data for the test workbench. A fuzzy logic based motorized window blind controller and look-up table based dimming of LED luminaires are used to set the required illuminance with reduced load on the heating, ventilation, and air conditioning system. The anticipated synergetic effects of the test workbench have been validated using real time climate data. The test work bench is established on a Labview platform and developed as a standalone system using myRIO.

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.

Daylight-electric light integrated schemes encompassing soft computing models have been perceived as a lucrative option for lighting energy conservation. This paper exploits the quintessence of design and real-time implementation of an adaptive predictive control strategy for robust control of a daylight-electric light integrated scheme. To elicit daylight variations, occupancy detection and user preferences an online self-adaptive, predictive control algorithm is structured for real-time control of electric lights and window blinds. The adaptive, predictive model entails integration of an online, adaptive daylight illuminance predictor in conjunction with an electric light intensity control algorithm for interior illuminance regulation and a fuzzy-logic based window blind control algorithm to eliminate glare and solar heat gain. The control algorithm modelled with real-time sensor information administers an online process of identification, prediction and parameter adaptation. The prototype controller is successfully implemented in a test chamber. A real-time user-friendly simulator provides an online visualisation of illuminance performance indicators and control of the process. The anticipated synergetic effects of the online control algorithm validated in the test chamber highlights the benefits of the scheme in terms of glare control, illuminance uniformity and energy efficiency.

Over a few decades, daylighting has been perceived to possess good potential for energy conservation. In this perspective, there have been significant advances in research methodologies and technologies for optimizing energy consumption through daylight harvesting in commercial buildings. In light of this, a thorough understanding of the application of available technology is very important for daylighting practices for building energy management. The objective of this paper is to examine the status of published research on three key building parameters: window glazing area, dynamic shading devices, and daylighting controls playing a rule on energy conservation. This article may serve as a coherent literature survey that would provide better understanding of the subjacent issues and possibly rejuvenate research interest in this immensely potential field of energy engineering.