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Short-term load forecasting(STLF) is of great importance for the safety and stabilization of grids. Based on the historical load data of meritorious power of some area in Guizhou power system, three BP neural networks in steepest descent algorithm back propogation neural network(SDBP), Levenberg -Marquardt algorithm back propogation neural network (LMBP) and Bayesian regularization algorithm back...
An Elman neural network with a weather component is proposed for the power load forecasting. Elman neural network can meet nonlinear recognition and process prediction of the dynamic system, and make power system having the ability to adapt to time-varying characteristics in mechanism. It is proved by simulation results that this model has a good performance in increasing forecasting accuracy because...
Short-Term Load Forecasting (STLF) is a very important aspect of power system to ensure operating safely economically and achieve scientific management in the power system. In this paper, Bayesian - BP Neural Network model has been designed for STLF. We used Bayesian - BP Neural Network to forecast the hour power load of weekdays and weekends. For doing this, Bayesian learning method has been used...
The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads...
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...
On account of the need of real-time monitor and alarm in smart grid, a new approach to ultra-real time intelligent computation of short circuit current is proposed. The extrapolation of grid state is conducted by ultra-short term load forecasting of nodes, and a short circuit current identification method based on GRNN is adopted to conduct the scan the short circuit current level of buses in short...
To settle the problem which the precision and generalization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors....
A short-term load forecasting model is adopted with a combined method. The model not only summarizes virtues and defects of neural networks and fuzzy system, but also considers that power system load has characteristics of basic load heft and variability load heft. It uses learned capability of neural networks to complete forecasting work of basic heft for power load. Other effect factors that cause...
The short-term load is nonlinear, and the change of it is influenced by various factors. Be one of them, the temperature is considered the main influencing factor. Not only the temperature of the day to be forecasted take a great influence on the load, but also the temperature of the previous days does. Especially in summer, the influence of the continuous high temperature on the load is different...
A. new forecasting model based on HHT and combination of ANN is proposed in the paper. Load data can be decomposed into several IMF components and remainder by EMD firstly. Through calculating the spectrum of decomposed series by Hilbert transform algorithm, we can choose one appropriate forecasting model for each low frequency component, while use combination of ANN model for the high frequency component,...
Power is important to modern society and national economy. To forecast short-term load more accurately, phase space of the complex nonlinear system was reestablished according to chaos theory and properties of short-term load were analyzed. It proves that forecasting short-term load is a classic decision-making process, full of chaos. Combining with chaos theory and traditional BP network, an improved...
Load forecasting model which synthetically considers every kind of impact factor is created in this paper. The input load data and temperature are normalized, and weather condition variable is quantitatively transacted. The applications of the BP (Back-Propagation Network) neural network algorithm and the neural network toolbox in MATLAB 7.0 software achieve load forecasting. The experimental result...
Electrical load forecasting is one of the important concerns of power systems and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ artificial neural networks (ANN) and regression (linear...
Considering the features of long term load forecasting are complicated, this paper proposes a generic neural network model that is able to adapt to and learn from amount of non-linear or imprecise rules, so it is a model with highly robustness. For avoiding the inflexibility of the generic neural network itself, many experiences and opinions of experts are introduced during the use, so that a comprehensive...
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...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
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...
Load forecasting is vitally important for the electric industry in the deregulated economy. Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. However, forecasting electricity load is difficult because...
This article shows that the effect of holiday on Thursday affects significantly the load behavior on the next Friday due to the Brazilian culture of joining the holiday with the weekend. A statistic test shows that there are differences between the Friday following the holidays and other Fridays. Also, using a legacy forecast system with usual MAPE (mean absolute percentage error) rates at 2.5%, forecasting...
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