Sensors 2010, 10(10), 9384-9396; doi:10.3390/s101009384
Article

Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

1 CSIRO ICT Centre, Corner of Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia 2 The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
* Author to whom correspondence should be addressed.
Received: 30 August 2010; in revised form: 15 September 2010 / Accepted: 25 September 2010 / Published: 18 October 2010
(This article belongs to the Special Issue Intelligent Sensors - 2010)
PDF Full-text Download PDF Full-Text [260 KB, Updated Version, uploaded 19 October 2010 09:29 CEST]
The original version is still available [133 KB, uploaded 18 October 2010 14:25 CEST]
Abstract: Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.
Keywords: multi-sensor; data fusion; dempster-shafer; prediction; renewable energy; virtual power station

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

Li, J.; Luo, S.; Jin, J.S. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory. Sensors 2010, 10, 9384-9396.

AMA Style

Li J, Luo S, Jin JS. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory. Sensors. 2010; 10(10):9384-9396.

Chicago/Turabian Style

Li, Jiaming; Luo, Suhuai; Jin, Jesse S. 2010. "Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory." Sensors 10, no. 10: 9384-9396.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert