In-Network Distributed Solar Current Prediction
Author(s)Basha, Elizabeth; Jurdak, Raja; Rus, Daniela L.
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Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this article, we present a model and algorithms for distributed solar current prediction based on multiple linear regression to predict future solar current based on local, in situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10[superscript -7%] of the harvested energy) to gain a prediction improvement of 39.7%.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
ACM Transactions on Sensor Networks
Association for Computing Machinery (ACM)
Elizabeth Basha, Raja Jurdak, and Daniela Rus. 2014. In-Network Distributed Solar Current Prediction. ACM Trans. Sen. Netw. 11, 2, Article 23 (December 2014), 28 pages.