✦ LIBER ✦

Grid performance prediction using state-space model

✍ Scribed by Mohammad Kalantari; Mohammad Kazem Akbari

Publisher: John Wiley and Sons
Year: 2009
Tongue: English
Weight: 466 KB
Volume: 21
Category: Article
ISSN: 1532-0626
DOI: 10.1002/cpe.1375

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Prediction intervals for exponential smo

Prediction intervals for exponential smoothing using two new classes of state space models

✍ Rob J. Hyndman; Anne B. Koehler; J. Keith Ord; Ralph D. Snyder 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 165 KB 👁 1 views

Three general classes of state space models are presented, using the single source of error formulation. The first class is the standard linear model with homoscedastic errors, the second retains the linear structure but incorporates a dynamic form of heteroscedasticity, and the third allows for non

Forecasting climate variables using a mi

Forecasting climate variables using a mixed-effect state-space model

✍ Philip Kokic; Steve Crimp; Mark Howden 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 261 KB 👁 1 views

Bayesian State Space Models for Inferrin

Bayesian State Space Models for Inferring and Predicting Temporal Gene Expression Profiles

✍ Yulan Liang; Arpad Kelemen 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 288 KB 👁 2 views

Using the job demands-resources model to

Using the job demands-resources model to predict burnout and performance

✍ Arnold B. Bakker; Evangelia Demerouti; Willem Verbeke 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 198 KB 👁 1 views

Using the job demands-resources model to

Using the job demands-resources model to predict burnout and performance

✍ Arnold B. Bakker; Evangelia Demerouti; Willem Verbeke 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 198 KB 👁 1 views

Using stochastic state-space modelling t

Using stochastic state-space modelling to specify and reduce hydrograph error

✍ C. G. Collier; G. L. Robbins 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 122 KB 👁 1 views

## Abstract A Bayesian post‐processor is used to generate a representation of the likely hydrograph forecast flow error distribution using raingauge and radar input to a stochastic catchment model and its deterministic equivalent. A hydrograph ensemble is so constructed. Experiments are analysed us