一个智能气象站。

An Intelligent Weather Station.

作者信息

Mestre Gonçalo, Ruano Antonio, Duarte Helder, Silva Sergio, Khosravani Hamid, Pesteh Shabnam, Ferreira Pedro M, Horta Ricardo

机构信息

EasySensing-Intelligent Systems, Centro Empresarial de Gambelas, Pav A5, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal.

Faculty of Science and Technology, Campus de Gambelas, University of Algarve, 8005-139 Faro, Portugal.

出版信息

Sensors (Basel). 2015 Dec 10;15(12):31005-22. doi: 10.3390/s151229841.

DOI:10.3390/s151229841

PMID:26690433

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4721761/

Abstract

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

摘要

准确测量全球太阳辐射、大气温度和相对湿度，以及获取它们随时间演变的预测数据，对于农业、可再生能源与能源管理或建筑热舒适性等不同应用领域而言至关重要。基于此，开发了一种智能、轻便、自供电且便携的传感器，采用最近邻（NEN）算法和人工神经网络（ANN）模型作为时间序列预测机制。文中描述了所实现原型的硬件和软件设计，以及在48步超前预测范围内，使用这两种方法针对三个大气变量的预测性能。