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首页> 外文会议>2014 International Conference on Advances in Energy Conversion Technologies : Intelligent Energy Management: Technologies and Challenges >An Adaptive predictive framework to online prediction of interior daylight illuminance
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An Adaptive predictive framework to online prediction of interior daylight illuminance

机译：在线预测室内日照度的自适应预测框架

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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：为了解决设计适当的采光控制器的开放性问题，对使用非线性技术进行室内日照度预测的兴趣日益浓厚。室内照度建模和预测方法提供了一种客观的方法，可以从时间序列模型中预测室内日照度的未来价值。考虑使用自适应预测技术的冲动在于这样一个事实，即日光本质上是高度动态和非线性的。该手稿阐明并评估了三种非线性模型的性能：非线性自回归（NLARX），时延神经网络（TDNN）和自适应神经模糊推理方案（ANFIS），可通过在线外部和内部传感器准确实时地预测室内日照度测量。通过采用在线RLS自适应算法对模型参数进行在线调整，可以缩小实际系统动力学与已识别模型之间的误差。从安装在Manipal（13°13'N，77°41'E）的试验箱外部和内部的各个照度传感器实验获得用于建模的外部和内部照度数据集。 NLARX，TDNN和ANFIS模型的预测结果已通过实时实验测量得到验证。本质上，三个模型的性能指标比较表明ANFIS是在线预测室内动态照度的有利工具。所提出的ANFIS计算预测模型的实践方面为在基于计算机/嵌入式系统的算法中耦合以充当实时人造光控制器提供了机会。

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