The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs.
In this paper, ANN and ANFIS based forecast model for predicting the PV Generation are presented.
In many EEMD based forecast models, the entire time series are decomposed into several sub-series, and each sub-series is divided into calibration and validation datasets and forecasted by some common models, such as artificial neural network (ANN), and finally an ensemble forecast is obtained by summing the forecasted results of each sub-series.
However, the forecast precision of most existing ANN based forecast models has not been satisfactory to researchers and engineers so far, and the generalization capability of these networks needs further improving.
Statistically based forecast models also failed to predict the evolution of this climatic anomaly during 1990 1993 in northeast Brazil (11).
We are convinced that the model presented here can be developed into more refined models of 'value' based forecasting of drug use in a healthcare region.
In the present work a spatiotemporal model, based on climatic forecast using the concept of growing degree days [ 16], retrospectively predicts a latitudinal and altitudinal spreading of the potential transmission area of Dirofilaria, mainly in the Russian Federation and Ukraine, near the boundaries where temperatures are close to the threshold for extrinsic incubation of parasite larvae.
In order to solve the false alarm problem in the diesel vapour detection and alarm system in engine room of ship, this paper builds an pre-alarm model based on grey forecasting theory.
However, a study by Willis and colleagues [ 42], using a population pharmacokinetic model based on Bayesian forecasting and adapted for individual pharmacokinetic, demographic, and covariate data, resulted in predictions that were too imprecise.
For example, one would hardly implement changes to a numerical weather forecast model based on a sample of just 20 forecasts.
