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