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AIn-Network Distributed Solar Current Prediction
| Content Provider | CiteSeerX |
|---|---|
| Abstract | 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 paper, 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 107 % of the harvested energy) to gain a prediction improvement of 39.7%. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Ain-network Distributed Solar Current Prediction Recent Local History 7-week-long Experiment Local Environment Harvested Energy Local Condition Current Solar Prediction Method Fleck Platform Energy Harvestable Multiple Linear Regression Solar Measurement Increased Energy Expenditure Algorithm Leverage Spatial Information Global Climatic Information High Variability Prediction Improvement Distributed Solar Current Prediction |
| Content Type | Text |
