Infinite hidden Markov model for short-term solar irradiance forecasting

Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assu...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Solar energy Ročník 244; s. 331 - 342
Hlavní autoři: Frimane, Âzeddine, Munkhammar, Joakim, van der Meer, Dennis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.09.2022
Elsevier
Témata:
Applied Mathematics and Statistics
Clear sky index
Electric power
Engineering Science with specialization in Civil Engineering and Built Environment
Engineering Sciences
Mathematics
Non-parametric Bayesian
Probabilistic solar forecasting
Probability
R-code
Statistics
Teknisk fysik med inriktning mot byggteknik och byggd miljö
Tillämpad matematik och statistik
Clear sky index
Probabilistic solar forecasting
Non-parametric Bayesian
R-code
ISSN:0038-092X, 1471-1257, 1471-1257
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify. In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler. Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon. For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material. •Infinite hidden Markov model is used for short-term forecasting of solar irradiance.•Various data sets from various regions are used to test the model predictive ability.•The model automatically learns the “correct” model complexity (number of states).
ISSN:0038-092X
1471-1257
1471-1257
DOI:10.1016/j.solener.2022.08.041