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Artificial neural networks to predict daylight illuminance in office buildings

机译：人工神经网络预测办公楼的日照度

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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. Neuro-Solutions 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.
机译：通过使用人工神经网络（ANN），开发了一种预测模型来确定办公楼的日照度。通过应用现场测量方法收集3个月的照度数据。利用来自当地气象站的天气数据和建筑图纸中的建筑物参数，构建了三层前馈型ANN模型（具有一个输出节点）。时间（日期，小时）的两个变量，5个天气因素（室外温度，太阳辐射，湿度，紫外线指数和紫外线剂量）和6个建筑物参数（与窗户的距离，窗户的数量，房间的方向，地板的标识，房间的尺寸）和点识别）视为输入变量。照度用作输出变量。在人工神经网络建模中，数据被分为两组：这些数据集中的前80个用于训练，其余20个用于测试。 Microsoft Excel规划求解使用单纯形优化方法获得最佳权重。然后通过使用照度百分比误差来测量模型的性能。由于模型的预测能力几乎为98％，因此预测数据与实测数据具有紧密匹配。在样品测量中，预测结果是成功的。然后对该模型进行敏感性分析，以确定输入和输出变量之间的关系。此应用程序采用NeuroDimensions Inc.的Neuro-Solutions软件。研究人员和设计师将从模型中受益，可以通过进行预测和比较来评估建筑物的采光性能，并可以通过确定照度在采光设计过程中受益。

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