This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...

An accurate and stable short-term traffic forecasting model is very important for intelligent transportation systems (ITS). The forecasting results can be used to relieve traffic congestion and improve the mobility of transportation. This paper proposes a new hybrid model of grey system theory and neural networks with particle swarm optimization, namely, GNN-PSO. The proposed hybrid model can e...

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique acc...

The Kohonen self-organization map is usually considered as a classification or clustering tool, with only a few applications in time series prediction. In this paper, a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting ...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

In this study, an ARX model with nonparametric multi time scale regression model was proposed for short term gust forecasting and applied to a ski resort in Japan. Proposed ARX model can be used for short term forecasting of mean and fluctuating wind speed. Proposed nonparametric multi time scale regression model can appropriately estimate the peak factor and shows better agreement with the mea...

This paper investigates the ability of a new hybrid forecasting model based on empirical mode decomposition (EMD), cluster analysis and Autoregressive Integrated Moving Average (ARIMA) model to improve the accuracy of fishery landing forecasting. In the first step, the original fishery landing was decomposed into a finite number of Intrinsic Mode Functions (IMFs) and a residual by EMD. The seco...

The climatology of September thunderstorms a t Denver, Colo., is discussed. Data as near as possible to the time of occurrence of thunderstorms are studied to determine variables related to the occurrence of thunderstorms. The same variables from earlier data are investigated to determine their forecasting value. Three variables taken from the 2000 MST raobs and 700-mb. charts are ultimately us...

Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to investigate the potential of support vector machines (SVM) model for streamflow forecasting at ungaged sites, and to compare its performance with other statistical method of multiple linear regression (MLR). Three quantitative stan...