Nonlinear Modeling of Solar Radiation and Wind Speed Time Series
Nonfiction, Science & Nature, Technology, Machinery, Science, Physics, Energy
| Author: | Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari | ISBN: | 9783319387642 |
| Publisher: | Springer International Publishing | Publication: | June 21, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Luigi Fortuna, Giuseppe Nunnari, Silvia Nunnari |
| ISBN: | 9783319387642 |
| Publisher: | Springer International Publishing |
| Publication: | June 21, 2016 |
| Imprint: | Springer |
| Language: | English |
This brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.— as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques.
The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques.
Results are well-illustrated by figures and tables.
This brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.— as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques.
The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques.
Results are well-illustrated by figures and tables.
