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Given the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its uncertainty by wavelet transform. To avoid...
By influencing the demand side by means of price signals (Demand Response) additional flexibility potential in electric supply systems can be provided. However, by influencing the demand side typical consumption patterns of previously unaffected consumers are changed. This will lead to increasing uncertainty in load forecasting. This paper deals with the forecast of load time series in consideration...
In this paper, we employed both traditional and chaotic approaches for time series forecasting. It concerns the forecasting of cash withdrawal amounts at automated teller machines (ATMs) for which the NN5 forecasting competitions data was used. The data consists of 111 time series representing daily withdrawal amounts. In the first method (traditional, non-chaotic) missing values of the time series...
Accurate irradiance forecasting is one of the essential factor that helps facilitate the proliferation of grid-connected photovoltaic (GCPV) integration. In Malaysia, this topic has not been substantially explored. This paper attempts to investigate the use of neural network by using data obtained from meteorological condition measurement in Sepang, Malaysia to forecast hourly values of solar radiation...
The detection of brain condition under different subjects is utmost important and it is a challenging task. EEG signals are such data that need to carefully analyze when it consists of series of different subjects. This paper explores the application of canonical correlation analysis with artificial neural networks for EEG data sets with different subjects and reference. We demonstrate the network's...
This paper aims to develop a methodology for choosing the inputs of a multilayer fuzzy inference system to forecast time series power demand values in a substation feeder. The forecast is done by analyzing past time series data. On an iteration process., older data with greater correlation with the previous forecast errors are the inputs of the fuzzy system., which has as output a future demand value...
Network traffic exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper compares predictions produced by different types of neural networks (NN) with forecasts from statistical time series models (ARMA,...
Purpose of the present paper is to examine the predictability of the occurrence of the severe pre-monsoon thunderstorm over Gangetic West Bengal. Instead of considering various meteorological predictors, the daily total ozone concentration is chosen as the predictor because of the influence of tropospheric as well as stratospheric ozone on the genesis of meteorological phenomena. Considering the occurrence/non-occurrence...
This paper proposes a faint signal processing approach combining AR model and BP neural network (NN), by which the faint signal is fitted with AR model, whose coefficient served as signal eigenvector, and then sent into a three-tier BP NN for training and recognition classification. Classification tests on human pulse signals between drug users and non-users show that this approach is characterized...
Long range dependence is closely linked with self-similar stochastic processes and random fractals, which have been considered extensively for signal processing applications and computer network traffic modeling. The Hurst parameter captures the amount of long-range dependence in a time series. Typically, the analysis of self-similar series is performed using: the variance-time plot, the R/S plot,...
The paper set up regional logistics prediction model based on the chaotic nerve network according to regional logistics characteristic, judge regional logistics chaotic characteristic Utilize phase space reconstruction technology at first, Positive Lyapunov exponent and correlation dimension prove the regional logistics has Chaotic characteristics. Then set up neural network prediction models on the...
This paper considers the problem of recovering time-varying sparse signals from dramatically undersampled measurements. A probabilistic signal model is presented that describes two common traits of time-varying sparse signals: a support set that changes slowly over time, and amplitudes that evolve smoothly in time. An algorithm for recovering signals that exhibit these traits is then described. Built...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
Research on time series forecasting has been an area of considerable interest in recent decades. Several techniques have been researched for time series forecasting. There is a fundamental task in any area of knowledge of time series: use past values to predict future values from the available historical series. Thus, a very important step is to define which of these past values will be considered...
As demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded...
This paper proposes the concept of function correlation, which takes the concept of linear correlation and concept of non-linear correlation together with a unified. On this basis, a BP neural network model was constructed to approximate the correlation function. Experimental results show that the correlation function determined by BP neural network can be a higher correlation coefficient.
This paper presents a new approach to short-term wind speed prediction. The chaotic time series analysis method is used to capture the characteristic of complex wind behavior in which a correlation dimension method is employed to calculate embedding dimension of the time series, then a mutual information method is used to determine the time delay. Based on the embedding dimension and time delay, support...
There are no algorithms that generally perform better or worse than random when looking at all possible data sets according to the no-free-lunch theorem. A specific forecasting method will hence naturally have different performances in different empirical studies. This makes it impossible to draw general conclusions, however, there will of course be specific problems for which one algorithm performs...
Coalmine gas explosion is unique to the extremely serious type of disaster. The root cause of gas explosion accident is the Overrun of the gas concentration. Gas concentration is forecast to achieve effective prevention of gas explosion accidents. According to the non-linear of gas concentration and the predictability of the chaotic time series, gas concentration phase space was reconstructed by the...
This paper presents an application of Artificial Neural Networks (ANNs) in the renewable energy domain and, more particularly, to predict solar energy. We look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. In previous studies, we have demonstrated that an optimized ANN with endogenous...
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