جستجوی مقالات مرتبط با کلیدواژه « رگرسیون بردار پشتیبان » در نشریات گروه « برق »

The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, the intrinsic mode functions (IMFs) of the Electric load curve, which are a group of average and pseudo-periodic average signals, are extracted by using the empirical mode decomposition (EMD) method, which is a non-linear and non-constant time-frequency method. For this purpose, the maximum and minimum points of the signal are determined, and then, in one cycle, the difference between the average curve of the upper and lower envelope is calculated with it. This continues until the result falls below a threshold value, and then, the rest of the signal which contains noise is discarded to get a relatively clean signal. In the second step, we need to obtain the sub-sequences of each IMF. So, we use the wavelet transform. The wavelet transform is a kind of transform that is used to decompose a continuous signal into its frequency components, and the resolution of each component is equal to its scale. Each subsequence contains different information and details that can help the improvement of the prediction accuracy. In the third step, the obtained subsequences are aggregated and finally used for prediction by Support Vector Regression (SVR). Support vector regression is a type of supervised learning system that is used for both grouping and estimating the fitting function of data in regression problems so that the least error occurs in the grouping of data or in the fitting function. The purpose of the proposed method is to reduce the error for daily and hourly load prediction. In this method, two datasets of Poland and Canada have been experimented. With four criteria of mean square error (MSE), root mean square error (RMSE), average absolute percentage error (MAPE) and mean absolute error (MAE), the results are evaluated. The findings show that the load prediction error for the Polish data set are as follows: MSE equal to 0.0012, RMSE equal to 0.0342, MAPE equal to 2.9771, and MAE equal to 0.0044. For Canadian data set, the results are as follows: MSE equal to 5.0969e-07, RMSE equal to 7.1393e-04, MAPE criterion equal to 0.9571, and the MAE criterion equal to 2624e-04. Comparison of the proposed method with other competing methods show that better results are achieved by the proposed method in term of the error rate.

In this paper, a method is presented for predicting time series. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with the greatest accuracy. The model presented in this paper is based on the combination of kernels and support vector regression. Support vector regression is highly capable of solving function estimation problems by using its kernels, but kernels’ parameters need to be adjusted. First we have preprocessing phase which includes normalizing data and separating data for testing and training. In proposed model, ten different kernels were used. Five kernels were selected as the best kernels by trial and error and these kernels are applied to data. There probably is only a few of the kernels that are useful for the problem, and we are not aware of which kernels are useful for our problem so kernel outputs aggregate by applying a coefficient. This combination creates a new secondary space. The output is given to support vector regression to construct a model that predicts values exactly ɛ accurate, which means the predicted values do not deviate more than ɛ from the original data. This model predicts values by using a leave one out model. Each kernel has parameters that need to be set to optimum values in order to get the best results. Hence in the proposed model, the kernel parameters and their weights are learned by the Gray Wolf Optimizer. This optimizer has been able to provide appropriate answers to many problems, especially challenging problems and has a superior ability to solve the high-dimension problems. By running program in consecutive iterations and examining the different values of the parameters, the optimizer learns the best of them which prediction error has been reduced, and finally returns their best value. The proposed model is implemented on five standard time series and compared to other method, test based on the RMSE criterion for DJ time series, improved by 1.58 point, Radio time series, improved by 0.178 point, and Sunspot time series, improved by 1.709 point. Finally, we analyzed the results, Statistical evaluation by Wilcoxon Signed-Rank Test where the p value is very low compared to the proposed method and CNN-FCM, AR_ model per scale, Multiresolution AR model and ANN methods, slightly lower for Wavelet-HFCM and ANFIS methods and slightly lower than one for SAE-FCM method and at the end provide a relation to find the window size in the model by obtaining the average of peak differences, valley differences, and consecutive peak, and valley differences for the actual values of the training data in exchange for their sequence number in time series.

In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as training data. In training phase, we apply support vector regression (SVR) to model the relationship between the pair of low and high-resolution images. For each patch in the low-resolution image, sparse representation is extracted as a feature vector. In this paper, in order to reduce the edge blurring effects, we first separate edge pixels from non-edge pixels. In the smooth area, because of the similar colors around the each pixel, the center pixel value is determined by considering the reconstructed adjacent pixels. Experimental results show that the proposed method is quantitatively and qualitatively outperform the competitive super-resolution approaches.

Increasing the number of processor cores leads to increasing the density of the computing power processor and also raising the temperature. Temperature management is very important in these processors. Thermal management methods are introduced to reduce the CPU temperature. Reactive and proactive approaches are two sets of these schemes. Unlike the reactive techniques, proactive methods predict the temperature using thermal prediction model before reaching its threshold. In this paper, a hybrid model of several SVR models is proposed for predicting temperature. An appropriate dataset is created for training proposed model that includes a high diversity of processor temperature variations. Some features of dataset are measured using temperature sensors and system performance counters. Other features, with historical and control names are calculated with the proposed processes to increase the accuracy of thermal model. Two SVR models are used in the proposed thermal model to reduce its operational overhead. The proper features for each SVR model are selected by the feature selection algorithm based on mutual information. The proposed model is evaluated for temperature prediction for 2 to 5 time distances. The results show that with a selection of 11 features for thermal prediction model of the next 2 seconds, the mean absolute error is about 0.5 °C.

Restructuring in power systems has caused electricity price forecasting became one of the most important challenges facing electricity market participants. The precise electricity price forecasting helps both consumers and producers to make better decision in order to maximize their benefit. In this paper, the historical data of electricity price and energy consumption are utilized for prediction of electricity price for the next 24 hours. The proposed intelligent algorithm is realized through three main steps: 1- preprocessing step, 2- feature selection and 3- forecasting step. At first, the price signal is decomposed to different modes by using Empirical Wavelet Transform (EWT). Afterward, in the second step, the feature selection method based on mutual information is applied on input data to improve the performance of forecasting engine. In the third step, for day-ahead hourly electricity price forecasting, the Support Vector Regression (SVR) is trained by selected features. The performance of the proposed algorithm is evaluated using real data of two electricity markets i.e. Pennsylvania New-Jersey Maryland (PJM) and Operador del Mercado Ibérico de Energía-Polo Español (OMIE).