Solar harvest prediction supported by cloud cover forecasts

Publikationstyp
Conference Paper
Date Issued
2013-11
Sprache
English
Author(s)
Renner, Bernd-Christian  
TORE-URI
http://hdl.handle.net/11420/13077
Citation
1st International Workshop on Energy Neutral Sensing Systems (ENSSys 2013)
Contribution to Conference
1st International Workshop on Energy Neutral Sensing Systems, ENSSys 2013  
Publisher DOI
10.1145/2534208.2534210
Scopus ID
2-s2.0-84891545640
Solar harvest prediction is used in energy-harvesting sensor networks to achieve perpetual node operation. Existing approaches only exploit local knowledge and thus fail in unforeseeable, changing weather conditions. We investigate the benefit of incorporating global knowledge in terms of fractional sky cloudiness, so-called cloud cover. We propose and evaluate two methods that combine local information of a node's harvest pattern with global cloud cover forecasts. We evaluate their performance with solar traces collected by three solar-harvesting sensor nodes and compare the results with existing prediction algorithms. We find that (i) harvest predictions using cloud cover forecasts improve overall prediction precision, (ii) prediction errors in changing weather conditions are considerably reduced, and (iii) coarse-grained cloud cover forecasts require low extra network traffic while sacrificing little prediction precision. © 2013 ACM.
Subjects
Algorithms
C.2.4 [Computer-Communication Networks]: Distributed Systems
Design
G.3 [Probability and Statistics]: Time-Series Analysis
Measurement
Performance