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Load forecasting is the first phase of electric power system planning for economic power generation-distribution, effective control and operation conditions of the system, and also energy pricing. In this study, short-term load forecasting, as the main tool for economic operation conditions, is realized. 24-hour-ahead load forecasting without temperature data for Turkey is aimed and structures with...
In a deregulated power industry, accurate short term load forecasting (STLF) and price forecasting (STPF) is a key issue in daily power market. The load forecasting helps in unit commitment as well as in economic scheduling of the generators. The price forecasting helps an electric utility to make important decisions like generation of electric power, bidding for generation, price switching and infrastructure...
In order to coordinate the heat supply of heat source and users' heat demand and also maintain the whole system work in a high efficiency, the wavelet analysis was utilized to make short-term prediction on heating load. The heating load of heat supply system was chosen as the output variation. After applying wavelet transform to the heat load sequence, the low-frequency signals and high-frequency...
In the analysis of power system with respect to the load forecast, the currently used methods appeared to be insufficient. Based on this, the wavelet analysis (WA) combined with the fuzzy support vector kernel regression method was proposed by considering the characteristics of the load power in load forecast. To start with, wavelet transform was employed to acquire the wavelet decomposition of power...
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 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 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...
Based on four general forecasting models (SVM model, BP neural network model, wavelet regression model and similar date model), two new models (integrated model I and integrated model II) are proposed in this paper. In the process of determining models and parameters, the virtual forecast conception is adopted. And a series of improvements on the aspects of historical data, temperature factor, holiday...
This paper put forward a new method of the 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 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...
In view of the power load with the randomicity and the complexity, the short-term power load forecasting based on optimal wavelet-particle swarm is introduced in this paper. First, the power load series is decomposed several frequency ranges by wavelet packet. Select the optimal wavelet tree to reconstruct the coefficients of the wavelet packet and form the number of power load components. Then, forecast...
The phase space reconstruction and artificial neural networks (ANN) coupled model is developed for flow forecasting in consideration of chaotic property and nonlinearity of flow series. 50 years of monthly flow data from 1950 to 1999 in Yichang hydrologic station is used for parameter calibration, and 4 years of the data from 2000 to 2003 is used for model validation. The result shows it has high...
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...
To improve the accuracy of load forecasting, a new algorithm is presented to forecast the short-term load. In the paper, short-time load sequence of the power supply system composed by different frequency signals is decomposed into the signals on different frequency bands by wavelets. Then the Radial Basis Function neural network (RBFNN) is used to forecast these signals in every scale space, and...
This paper presents a short-term load forecasting method based on wavelet neural network (WNN) and monkey-king genetic algorithm (MK). Parameters of WNN are mostly selected artificially or obtained through experiment time after time. A certain and effective method has not been found. Aiming at solving this problems, a method optimizing the WNN parameters with monkey-king genetic algorithm (MKWNN)...
This paper put forward a new method of the wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. A wavelet network is composed by the wavelet basis function. The global optimum solution is got. We overcome the intrinsic defects of a artificial neural network that its learning speed is slow, its network structure is difficult to determine...
This paper proposes a new method for load forecasting - the wavelet neural network model for load forecasting. The neural call function is basis of nonlinear wavelets. A wavelet network is composed by the wavelet basis function. The global optimum solution is got. We overcome the intrinsic defects of a artificial neural network that its learning speed is slow, its network structure is difficult to...
In this paper, a wavelet-neural-network-based forecast model is developed for energy demand in China. Combining qualitative with quantitative analysis, we analyze some main factors affecting energy demand in China. A first order wavelet-neural network forecasting model with time-delay is established, including population, GDP, variation of industrial structure and energy consumption. The simulation...
In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory...
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