The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Wind power forecasting has great significance to the connection of wind farms to the electric power system. This paper analyzes individual forecast models, such as the time series forecasting, Elman network forecasting that based on the chaos theory, grey neural network forecasting, and generalized regression neural network forecasting, etc., then puts forward an entropy weight combination prediction...
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....
In this paper, an approach is proposed for time series prediction based on modified probability neural network (MPNN). Bayesian-statistics and decision-making theories and non-parameters density function estimation using Parzen-window function are applied to MPNN. The efficiency of the approach was demonstrated by a case study, an application for prediction on moonlet power system data, through comparison...
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,...
In this paper, the basic idea is to use percent of reserve capacity, the historical load and price to forecast short-term electricity price .The paper provides an example of bidding model to forecast market clear price using BP neural network trained by PSO. To compare with the result of traditional BP neural network, the proposed method has better forecasting precision and can convergence to global...
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...
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...
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...
In this study, unit commitment (UC) problem is solved for an optimum schedule of generating units based on the load data forecasted by using artificial neural network (ANN) model and ANN model with autoregressive (AR). Low-cost generation is important in power system analysis. Under forecasting or over forecasting will result in the requirement of purchasing power from spot market or an unnecessary...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.