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This paper proposes a composite method for short-term load forecasting, which is based on fuzzy clustering wavelet decomposition and BP neural network. Firstly, the similar-day's load is selected as the input load based on the fuzzy clustering method; secondly, the wavelet method is applied to decompose the similar-day load into the low frequency and high frequency components, from which the feature...
This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All...
This paper put forward a new method of the fuzzy rules and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of fuzzy rules. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that...
This paper put forward a new method of the fuzzy rules and wavelet neural network model for mid-long term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of fuzzy rules. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that...
This paper put forward a new method of the SVM and fuzzy rules model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to...
This paper put forward a new method of the SVM and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective...
This paper proposes a new method for load forecasting-fuzzy rules by genetic algorithms based on Takagi-Sugeno Fuzzy Logic System, and establishing the fuzzy model for load forecasting. It can be seen from the example this method can improve effectively the forecast accuracy and speed. It can be applied to the short-term electric load forecasting.
This paper present a comparative study between ANFIS, neuro-fuzzy (NF) and artificial neural network (ANN) approaches, applied to STLF algorithm (one hour ahead). Distribution networks need reliable short-term load forecast. The STLF algorithms associated with network management, as load dispatch and network reconfiguration, under quality of service constraints, improves the maintenance issues and...
In view of the defects of the prediction model based on neural network, such as when doing prediction of nonlinear sequence, it is likely to fall into local hypo-strong point, and the rate of training is very slow, based on the traditional prediction model using neural network, a novel short-term electrical load prediction model based on fuzzy wavelet neural networks (FWN) is presented in this paper...
Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos in electricity systems to a great extent. Complicated electricity systems are nonlinear systems and the forecasting is very complex in nature and quite...
Today, it's the need of developed and developing countries to consume electricity more efficiently. Though developed countries do not want to waste electricity and developing countries cannot waste electricity. Hence, the wise use of electricity is the need of hour. This leads to the concept - load forecasting. This paper is written for the short term load forecasting on daily basis. Though this can...
Multivariate inputs play important role in system with many dependent variables. By using some different inputs as input in neuro-fuzzy networks, complex nonlinear model can be modeled and also be forecasted with better results. This paper describes a neuro-fuzzy approach with additional fuzzy C-means clustering method before the input entering the networks. Afterwards, the network can be used to...
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network...
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