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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...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
In this paper, an automatic quality inspection system for the riveting process by using quantum neural network (QNN) was proposed. This inspection system not only can monitor the real time riveting process, but also can give the assistance on the riveting quality verification. For demonstrating the superiority of the inspection system we developed, the data provided by the experiment did by Chinese...
This paper put forward a new method of the variable structure artificial neural network model for mid-long term load forecasting. We overcome the shortcoming of single train set of ANN. 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 forecast mid-long term electric...
This paper put forward a new method of the SVM and variable structure artificial 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...
Support vector machines (SVM) has been used in load forecasting field. The noise and redundancy of sample data are important factors to the generalized performance of SVM. They can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting method for short-term load forecasting based on rough sets (RS-SVM) is developed in this paper, using rough sets algorithm...
Very short term load forecasting predicts the load over one hour into the future in five minute steps, and is important in resource dispatch and area generation control. Effective forecasting, however, is difficult in view of noisy real-time data gathering and complicated features of load. This paper presents a method based on multilevel wavelet neural networks with novel pre-filtering. The key idea...
This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy...
Because power loads are influenced by various factors, and the changes of power load presents are complicate, the traditional forecasting methods are always not satisfied. According to the random-increase and non-linearity fluctuation of residual series, gray neural network forecasting can reflect the increase character and non-linearity relationship. This paper using the improved ACO method as the...
In order to establish a high accuracy forecasting model for short-term electric power load, this paper made a change to grey differential equation utilizing the fundamental theorem of discrete time function. Through mapping the parameters of the equation into the BP neural network, giving the corresponding parameters when the sequence sample of load was converged in the network. In this case, optimizing...
Neural network can increase forecasting accuracy of power system load , but canpsilat provide explanation for forecast reason, so this paper proposes a short-period load forecasting method based on structural neural network. The paper respectively set up such models as historical load data forecasting model, weather forecasting model and date type model. First three models are respectively learned...
This paper compares the ability of six artificial neural networks to predict hourly system load for the Puget Sound Power and Light Company, a major North American electric utility. The neural nets, along with four other types of models, were used to forecast hourly system load for the next day on an hour by hour basis. This was done for the period November 1, 1991 to March 31, 1992.<<ETX>>
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