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This paper proposes a novel approach for short-term wind power forecast, where wind speed is predicted and used to forecast wind power through a power curve obtained from historical data. With the help of the empirical mode decomposition (EMD) method, wind speed is decomposed into mean trend and stochastic component. Subsequently, p-step forecast is conducted for the two components separately. The...
This paper proposes a wind power prediction method based on intrinsic time-scale decomposition (ITD) and least square support vector machine (LS-SVM) to improve the accuracy of wind power forecast. The proposed method employs ITD as a preprocessing method to decompose wind power data into a set of proper rotation components and a monotonous baseline signal. Afterwards, the backward difference of each...
This paper presents two improved models based on the first-order multi-variable grey model (GM(1, N)) for forecasting the electricity demand. The first model named IGM1(1, N) is developed through the optimization of background value by Lagrange mean value theorem (LMVT). Another model named IGM2(1, N) is established through the calculation of its boundary value using least square method (LSM). Despite...
This paper presents a novel fault diagnosis model for oil-immersed power transformers based on dissolved gas analysis. The model is rooted on the theories of rough set and support vector machine. A fitness function based on attribute dependence is developed to identify fault features to improve classification accuracy of transformer fault samples by using Genetic Algorithm. To get improved classification...
This paper proposes a novel forecasting model based on a mean trend detector (MTD) and a mathematical morphology-based local predictor (MMLP) to undertake short-term forecast of wind power generation. In the proposed MTD/MMLP model, the nonstationary time series describing wind power generation is first decomposed by the MTD, which employs some new notions and conventional morphological operators...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
This paper proposes a pre-processing method to enhance the accuracy of wind power forecast. Instead of using the whole dataset indifferently for training, the proposed method only uses the segments that share the same pattern. In order to search for such segments in the historical data, a k-OCCO filter and a weighted multi-resolution morphological gradient (MMG) are employed. Afterwards, the forecast...
Wind power prediction has received much attention due to the development renewable energy sources using wind power. The paper presents a new approach which is a support vector regression (SVR) based local predictor (LP) with false neighbours filtered (FNF-SVRLP) to undertake short-term wind power perdition. The proposed predication method not only combines the powerful SVR with the reconstruction...
A day-ahead market clearing price forecasting method based on the Takagi-Sugeno model and the adaptive neuro-fuzzy inference system (ANFIS) is proposed. First, the structure of ANFIS is determined by subtractive clustering; then the premise parameters and consequent parameters of ANFIS are identified by the hybrid learning algorithm; finally, related factors that influence future daily electricity...
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...
One of the aims of the Protein Structure Initiative (PSI) in the post genome-sequencing era is to elucidate biochemical and biophysical functions of each protein structure. Thus, the development of new methods for a large-scale analysis/annotation of protein functional residues is inevitable. Currently existing methods are not capable to do so due to the lack of automation, availability, and/or poor...
This paper proposes a new approach to solve the short term load forecasting problem that considers electricity price as one of the main characteristics of the system load. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with locally weighted support vector regression (LWSVR). LWSVR can be derived by modifying the risk function of the support vector...
Mobile commerce systems create a new mobile business model and change e-commerce paradigms, having an especially significant effect on the medical and insurance industries. Furthermore, the real estate industry is increasing in the booming market, but tends to become overheated. Thus, some innovative techniques (such as mobile commerce) were adopted by estate agent to enhance their competitive advantage...
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