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Power load forecasting has always been a hotspot. Recently, Artificial intelligence and computational intelligence methods have been widely used in the power load forecasting field. SVR (Support Vector Regression), one of computational intelligence methods, has been paid more and more attention for its ability of solving none-liner problem and its high prediction accuracy. Most predicting methods...
Load forecasting is a critical necessity in the electricity industry since any unanticipated demand could cause possible grid instability and blackouts. Ideally, the capacity should be kept slightly above the current demand to avoid any undesired outages and suboptimal last minute power purchase. Motivated to develop an intelligent and efficient forecasting approach, we propose investigating in this...
Short-term load forecasting is important for the safety and economic operation of the wind power system. In order to forecast the power load more accurately, the Support Vector Machines (SVM) combined with the lifting wavelet transform is proposed in this paper. The lifting wavelet transform is used to find out the characteristics of original signal while the SVM is utilized to improve the precision...
At present, the support vector machine (SVM) has been successfully applied in the field of electric load forecasting, but most of the load forecasting models are based on the day characteristics of meteorological factors, without considering a real-time weather factors which are valuable real-time information, while the prediction accuracy and generalization ability are influenced by the sample set...
For the load affected by many factors and near the far smaller feature, a hybrid load forecasting method based on fuzzy support vector machine and linear extrapolation is proposed. The similar day is selected by the integrated effects of meteorology and time, and the fuzzy membership of the corresponding training sample is obtained by normalized similarity. Using the fuzzy support vector machine to...
Because traditional prediction algorithm can not accurately forecast long-term electricity load, chaos SVM prediction algorithm was introduced and some of its characteristics were discussed. The kernel function was chosen under the guidance of the geometric information. The experiment shows that the algorithm is more accurate and effective than the others.
A short-term load forecasting method based on least square support vector machine(LS-SVM) combined with fuzzy control was proposed. The peak load and valley load was forecasted by LS-SVM model which was built by analysis of load data and meteorological data. Then the peak load and valley load was tuned by fuzzy rules which has been built by forecasting error data. One day and one week ahead load has...
An Improved least squares support vector machine (LS-SVM) algorithm is proposed for 24 points electric load forecasting. First of all, facing with the problem how to choose the optimal LS-SVM algorithm parameters, an improved LS-SVM algorithm based on chaos optimization is put forward to obtain the optimal LS-SVM algorithm parameters and corresponding model parameters. Then, a method of 24 points...
In order to improve the accuracy of power load forecasting, this paper proposes a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD), least square-support vector machine (SVM) and BP nature network as a short-term load forecasting model. At first, the actual power load series is decomposed into different new series based on EEMD. Then the right parameters and kernel functions are chosen...
An intelligent forecasting method for local short term electric load is proposed based on fuzzy clustering and partitioned support vector regression (PSVR). Firstly, the scheme of this method is introduced. Then, prediction time was partitioned into three sections adaptively by means of fuzzy C-means clustering, according to the characteristics of local power load pattern. Sub-models for each section...
Accurate load forecasting is essential for energy planning and load management. This paper presents long term industrial load forecasting (LTLF) using Nonlinear Autoregressive Exogenous model (NARX) based Feed-Forward Neural Network (FFNN) method, Support Vector Regression (SVR) and Neural Network models. It is applied to data sets obtained from National Transmission and Dispatch Company (NTDC) of...
This paper represents comparison of two artificial intelligence based hybrid models for short term load forecasting (STLF). Models have the same input/output architecture and are built on SVM and ANN technologies, respectively. Algorithm consists of two modules connected in a sequence, and output from first module is connected as additional input to second module. First module acts as a predictor...
Short term load forecasting (STLF) is an important process in electric power operation and control system. Support Vector Regression (SVR) is proved to be a successful application in STLF, and can get great accuracy and efficiency compared to other STLF models. However, when deal large scale sample size, SVR is poor on the performance. With the development of cloud computing, it is changing people's...
In order to forecast the integrated load model of substation with a certain random time variation character and increase the accuracy of forecasting, this article put forward a forecasting method of electrical consumption proportion of different industries in substation based on the daily load curve. First of all, load sequence is decomposed into a number of different frequency stationary components...
Load forecasting plays a very important role in building out the smart grid, and attracts the attention of not only the researchers and engineers, but also governments. The classical method for load forecasting is to use artificial neural networks (ANN). Recently the use of support vector machines (SVM) has emerged as a hot research topic for load forecasting. In this study, in which several different...
This paper proposes a new hybrid intelligent method for probabilistic short-term load forecasting (STLF) in power systems. It consists of Relevance Vector Machine (RVM) of the statistical learning method called Kernel Machine and regression tree (RT) of data mining. As the preconditioned technique of data, RT is used to classify learning data into some clusters with the data similarity. After classifying...
As a typical and special complexity gigantic system, the power system is facing the challenge from complexity science in the aspects of load forecasting and its management. Therefore, on the basis of complex system theory, a new method used for predicting the short-term load is proposed by means of a series of subsystems divided according to the different areas and types of regional electricity. Support...
Accurate short term load forecasting plays a very important role in power system management. As electrical load data is highly non-linear in nature, in the proposed approach, we first use a Reproducing Kernel Hilbert Space (RKHS) method to fit the data. Afterwards a template is constructed based on the input-output data and the results from the RKHS method. To predict the load, only the template is...
Support vector machines (SVM), which are based on statistical learning theory and structural risk minimization principle, according to limited sample information, search the best compromise between the model complexity and the learning ability, and have good prediction effect. However, in the methods of load forecasting which are based on SVM, the choices of penalty coefficient c, insensitive coefficient...
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data. In the paper, by using the Bays estimate method, the weight of every forecasting model is obtained. Support Vector machines and Spectrum analysis are selected to construct the Bays combined model, which are applied to forecast. The forecasting method gives bigger...
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