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In power market, accurate electricity price forecasting can help all market participants make optimal bidding or purchasing decisions and maximize their revenue. In recent years, much attention has been focused on the short-term electricity price forecasting. Based on the theory of ARMA-GARCH model, the paper divides the constant day series into working day series and holiday series. Then the models...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
The paper proposes short-term power load forecasting model based on fuzzy RBF neural network, it has overcome the BP algorithm's disadvantage of slow convergence rate and it fall into partially the smallest insufficiency easily. RBF network model in the use of the latest neighborhood clustering algorithm, and the network structure and the parameters are double-adjusted and the training speed and forecast...
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...
Short-term electricity load forecasting is important both from the technological and the economical point of view, but it is also a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis)...
A gray model and regression model based middle and long term load forecasting method using variable weight combination model is proposed. In view of the shortcomings of grey prediction model is not very suitable for middle and long term load forecasting, the equivalent dimensions additional data processing technology is adopted to build the equivalent dimensions additional grey model to improve the...
The theory of quadratic variation suggests that, realized volatility is an unbiased and highly efficient estimator of return volatility under suitable conditions. In this article, we compare the realized logarithmic volatilities models VAR-RV and AR-RV computed from high-frequency intra-period data with the traditional daily return evaluation models VAR-R and Daily-GARCH in China A-stock market. The...
The use of neural networks (NNs) for financial applications is quite common because of their excellent performances of treating non-linear data with self-learning capability. Often arises the problem of a black-box approach,i.e. after having trained neural networks for a particular problem, it is almost impossible to analyse them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules...
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...
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. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machine (SVM) is a novel type of learning...
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