Abstract
The point prediction quality is closely related to the model that explains the dynamic of the observed process. Sometimes the model can be obtained by simple algebraic equations but, in the majority of the physical systems, the relevant reality is too hard to model with simple ordinary differential or difference equations. This is the case of systems with nonlinear or nonstationary behaviour which require more complex models. The discrete time-series problem, obtained by sampling the solar radiation, can be framed in this type of situation. By observing the collected data it is possible to distinguish multiple regimes. Additionally, due to atmospheric disturbances such as clouds, the temporal structure between samples is complex and is best described by nonlinear models. This paper reports the solar radiation prediction by using hybrid model that combines support vector regression paradigm and Markov chains. The hybrid model performance is compared with the one obtained by using other methods like autoregressive (AR) filters, Markov AR models, and artificial neural networks. The results obtained suggests an increasing prediction performance of the hybrid model regarding both the prediction error and dynamic behaviour.
Highlights
Often the output observation of a stochastic process can not be associated with any exogenous excitation variable
This paper addresses the prediction problem by using support vector regression (SVR) techniques, the autoregressive hidden Markov model, and an hybrid technique that combines both SVR and Markov chains
The maximum prediction horizon taken is sixty steps ahead and the model performance is inferred taking into consideration two indexes: the average of the root-mean squared (RMS) prediction error and the percentage of change in direction (PCD). The latter is a qualitative index representing the model ability to predict the tendency. This figure of merit is very important in the context of air temperature regulation under a model predictive controller (MPC), since the heating and ventilation requirements will be computed taking into
Summary
Often the output observation of a stochastic process can not be associated with any exogenous excitation variable. Autoregressive models, which only define linear relationships between past and present observations, represent one of the first attempts to explain the operating mechanism of stochastic processes [1]. Such representations are unable to adapt to complex situations as the ones that involve nonlinear relationships between observations or even the existence of various operating regimes [2]. In this sense, the solar radiation prediction is one of these complex problems.
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