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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 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...
A flexible neural network which includes flexible, S parameter-varying function is proposed owing to the defect of the tradition neural network in order to enhance the study speed and generalization of the flexible neural network. Action function of flexible function is called S-type function that contains monopole and bipolar. There, The bipolar flexible neural S-type function is adopted. It gives...
In the future Internet of Things devices will generate massive amounts of data that will flow to enterprise systems and provide a timely view on the execution of business processes. Being able to estimate data generated by devices may have significant effects on planning and execution of business applications. We present some methodologies for mining data gathered from devices in the energy domain...
For non-linear and gray of power load forecasting, this paper proposed a new combining forecasting model. First optimize the parameters of the GM(1, 1, ??) forecasting model with ant colony algorithm, and predict a set of load values; then predict another set of load values with Auto-regressive integrated moving average model (ARIMA). The forecasting results of ant colony gray model and ARIMA model...
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...
According to the maximum entropy principle and the characteristic of wind farm power series, a combined wind power prediction model was proposed. The wind power series is non-gauss distribution, so high central moment were added to prediction model besides the second central moment. The prediction results showed that the proposed model can improve the prediction precision.
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...
In order to study and improve the emission performance of WCS/diesel DFE, an emission model for DFE based on radial basis function neural network was developed which was a black-box input-output training data model not require priori knowledge .Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. And...
This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008...
Large-scale Generating Unit in heat power is a system which is complex nonlinear, multivariable, time-variant with long-time delay and difficult to establish accurate model, and etc. So it is hard to make system gain optimum running effect with conventional control strategy. A PID network which has dynamic character is used to identify the coordinated control system for establishing a predictive model...
Time series forecasting is an important aspect of dynamic data analysis and processing, in science, economics, engineering and many other applications there exists using the historical data to predict the problem of the future, and is one considerable practical value of applied research. Time series forecasting is an interdisciplinary study field, this paper is under the guidance of the introduction...
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...
Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or...
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...
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 the power load forecasting by using neural models for Toronto area, Canada. Different neural models were used to carry out the forecasting works. One-day-ahead daily total load and peak load forecasts were implemented by using different neural models in order to find the more accurate forecasting results. The load data and temperatures provided by Independent Electricity System...
Load forecasting is very essential to the operations of electric companies. This paper presents a rapid electric load forecasting algorithm based on Particle Swarm Optimization (PSO) and Core Vector Regression (CVR), called PSO-CVR algorithm. PSO is applied to determine the parameters of CVR, then CVR manages the issues of forecasting and training. In order to compare the results among different size...
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