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An Adaptive predictive framework to online prediction of interior daylight illuminance

机译：内部日光照度在线预测的自适应预测框架

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Aiming to solve an open problem of designing a appropriate daylighting controllers, there has been growing interest in the use of nonlinear technique to perform prediction of interior daylight illuminance. Interior illuminance modeling and prediction approach provides an objective way to predict the future value of interior daylight illuminance from time series model. The urge to consider adaptive predictive technique lies in the fact that daylight is highly dynamic and nonlinear in nature. This manuscript elucidates and evaluates the performance of three nonlinear models: Nonlinear Autoregressive (NLARX), Time Delay Neural Network (TDNN) and Adaptive Neuro Fuzzy Inference Scheme (ANFIS) for accurate real time series prediction of interior daylight illuminance from online exterior and interior sensor measurements. By adopting an online tuning of model parameters by an online RLS adaptation algorithm, error between the actual system dynamics and identified model is scaled down. The exterior and interior illuminance data set for modeling are experimentally acquired from respective illuminance sensors mounted outside and inside the test chamber at Manipal (13°13'N, 77°41'E). NLARX, TDNN and ANFIS model prediction results have been validated with the real time experimental measurements. In essence, performance index comparisons of three models indicate ANFIS as a lucrative tool for the online prediction of the dynamic interior illuminance. A practical aspect of proposed ANFIS computational prediction model elevates an opportunity to couple within computer/embedded system based algorithms to perform as a real time artificial light controllers.
机译：旨在解决设计适当的日光控制器的开放问题，对使用非线性技术来执行内部日光照度预测的兴趣越来越感兴趣。室内照度建模和预测方法提供了一种客观方式来预测室内日光照度的未来价值。考虑自适应预测技术的冲动在于日光在自然界中具有高度动态和非线性的事实。此手稿阐明并评估了三种非线性模型的性能：非线性自动增加（NLARX），时间延迟神经网络（TDNN）和自适应神经模糊推理方案（ANFIS），用于从网上外部和内部传感器的内部日光照度的准确实时序列预测测量。通过通过在线RLS自适应算法采用模型参数的在线调整，实际系统动态和识别的模型之间的错误缩小。用于建模的外部和内部照度数据从安装在Manipal（13°13'n，77°41'e）外部和测试室内安装室内的各个照度传感器的外部和内部照度数据。 NLARX，TDNN和ANFIS模型预测结果已被实时实验测量验证。从本质上讲，三种模型的性能指数比较表示ANFIS作为用于动态内部照度的在线预测的利润丰厚工具。所提出的ANFIS计算预测模型的实际方面提升了在基于计算机/嵌入式系统的算法内耦合的机会，以作为实时人造光控制器执行。

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