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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...
The principle and step of performance evaluation of project management based on fuzzy rules and wavelet neural network are studied. The index system of performance evaluation of project management is set up. Then we built up the evaluation model on fuzzy rules and wavelet neural network. Finally, take some samples of project for an example, we carry on this model to instance. It can take a preferably...
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...
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...
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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