Sensors | |
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes | |
Juan Pardo1  Francisco Zamora-Martínez2  Paloma Botella-Rocamora2  | |
[1] ESAI—Embedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera, C/San Bartolomé, 46115 Valencia, Spain; | |
关键词: wireless sensor networks; artificial neural networks; on-line Back-Propagation; ambient intelligence; energy efficiency; | |
DOI : 10.3390/s150409277 | |
来源: mdpi | |
【 摘 要 】
Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual,
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190013616ZK.pdf | 4409KB | download |