This paper proposes using the genetic algorithms to optimize the PI regulator parameter in the prime mover simulation system. In this paper, we compared the step response characteristics under the conditions of the genetic algorithms and traditional method by MATLAB simulation and field test tested the dynamic characteristics of the prime mover simulation system. The results proved that genetic...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...

The subject of this paper is the multi-step prediction of the Portuguese electricity consumption profile up to a 48-hour prediction horizon. In previous work on this subject, the authors have identified a radial basis function neural network one-step-ahead predictive model, which provides very good prediction accuracy and is currently in use at the Portuguese power-grid company. As the model is...

Model validation is an important step in system identification process. However, theoretical derivation of model validity tests for neural network such as RBF network is very complicated. The current study, investigate the capability of some of the model validity tests that are widely been used namely one step ahead prediction, model predicted output, means square error and correlation tests. T...

In this paper we introduce Quasi Likelihood Ratio tests for one sided multivariate hypotheses to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. We show that the limiting distributions of the test statistics are non standard. For critical values we consider two approaches: (i) boostrapping and (ii) simulations assuming norm...

Ensemble methods for classification and regression have focused a great deal of attention in recent years. They have shown, both theoretically and empirically, that they are able to perform substantially better than single models in a wide range of tasks. We have adapted an ensemble method to the problem of predicting future values of time series using recurrent neural networks (RNNs) as base l...

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....

NLARX (NonLinear AutoRegresive with eXogenous inputs) models are frequently used in black-box nonlinear system identi cation. Though it is easy to make one step ahead prediction with such models, multiple steps prediction ids far from trivial. The main di culty is that in general there is no easy way to compute the mathemaical expectation of an output conditioned by past measurements. An optima...