Name
Abstract | ||
|---|---|---|
Solar irradiance prediction is a major issue in energy harvesting enabled WSNs. In this paper, we use Markov chains of increasing order to propose a new model - referred to as ASIM - for predicting solar irradiance patterns. Cornerstone of the proposed model is the determination of the state dependencies of the underlying Markov chains. The ASIM model is derived from a comprehensive solar radiation data set of four different locations around the globe. Our trace driven performance evaluation reveals that the ASIM model predicts the solar irradiance pattern very accurately - Normalized RMSE as low as $0.1$ - as the order of the underlying Markov model increases. We also present a mechanism to reduce the implementation complexity thus making the model amenable for implementation in wireless sensor networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2820645.2820646 | ENSSys@SenSys |
Field | DocType | Citations |
Normalization (statistics),Markov model,Markov chain,Solar energy,Mean squared error,Energy harvesting,Real-time computing,Solar irradiance,Engineering,Wireless sensor network | Conference | 1 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Faizan Ghuman | 1 | 1 | 0.34 |
| Adnan Iqbal | 2 | 37 | 5.84 |
| Hassaan Khaliq Qureshi | 3 | 95 | 18.16 |
| Marios Lestas | 4 | 120 | 17.84 |