A prediction model for daylighting illuminance for office buildings

Thesis (Master)--İzmir Institute of Technology, Architecture, İzmir, 2008Includes bibliographical references (leaves: 94-100)Text in English; Abstract: Turkish and Englishxii, 130 leavesDaylight is a primary light source for the office buildings where a comfortable and an efficient working environment should be provided mostly during day time. Evidence that daylight is desirable can be found in research as well as in observations of human behavior and the arrangement of office space. A prediction model was then developed to determine daylight illuminance for the office buildings by using Artificial Neural Networks (ANNs). A field study was performed to collect illuminance data for four months in the subject building of the Faculty of Architecture in .zmir Institute of technology. The study then involved the weather data obtained from the local Weather Station and building parameters from the architectural drawings. A three-layer ANNs model of feed-forward type was constructed by utilizing these parameters. Input variables were date, hour, outdoor temperature, solar radiation, humidity, UV Index, UV dose, distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification. Illuminance was used as the output variable. The first 80 of the data sets were used for training and the remaining 20 for testing the model. Microsoft Excel Solver used simplex optimization method for the optimal weights. Results showed that the prediction power of the model was almost 97.8%. Thus the model was successful within the sample measurements. NeuroSolutions Software performed the sensitivity analysis of the model. On the top of daylight consideration, this model can supply beneficial inputs in designing stage and in daylighting performance assessment of buildings by making predictions and comparisons. Investigation about this subject can be able to support the office buildings. having intended daylighting comfort conditions

A prediction model for daylighting illuminance for office buildings

Thesis (Master)--İzmir Institute of Technology, Architecture, İzmir, 2008Includes bibliographical references (leaves: 94-100)Text in English; Abstract: Turkish and Englishxii, 130 leavesDaylight is a primary light source for the office buildings where a comfortable and an efficient working environment should be provided mostly during day time. Evidence that daylight is desirable can be found in research as well as in observations of human behavior and the arrangement of office space. A prediction model was then developed to determine daylight illuminance for the office buildings by using Artificial Neural Networks (ANNs). A field study was performed to collect illuminance data for four months in the subject building of the Faculty of Architecture in .zmir Institute of technology. The study then involved the weather data obtained from the local Weather Station and building parameters from the architectural drawings. A three-layer ANNs model of feed-forward type was constructed by utilizing these parameters. Input variables were date, hour, outdoor temperature, solar radiation, humidity, UV Index, UV dose, distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification. Illuminance was used as the output variable. The first 80 of the data sets were used for training and the remaining 20 for testing the model. Microsoft Excel Solver used simplex optimization method for the optimal weights. Results showed that the prediction power of the model was almost 97.8%. Thus the model was successful within the sample measurements. NeuroSolutions Software performed the sensitivity analysis of the model. On the top of daylight consideration, this model can supply beneficial inputs in designing stage and in daylighting performance assessment of buildings by making predictions and comparisons. Investigation about this subject can be able to support the office buildings. having intended daylighting comfort conditions

Artificial neural networks to predict daylight illuminance in office buildings

A prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance