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Price forecasting in competitive electricity markets plays a crucial role for any decision making. This is a difficult task since price time series are non-stationary, and with variable mean and variance, and also have periodic monthly and seasonal behavior. This paper introduces an approach to forecast several-hours-ahead electricity locational marginal price (LMP) using locally linear neuro-fuzzy...
This electronic For the limitations of dependence on previous experience and neural network forecasting model in current thunderstorm prediction. Considering the characteristics of the thunderstorm in Chongqing, the thunderstorm prediction model based on least square support vector machine (LS-SVM) is established. The data are preprocessed by principal component analysis(PCA) firstly. Then, the search...
The key problem of combination forecasting model is the weight of the single prediction methods. The values of the weights directly effect the accuracy of combination forecasting model. In this paper, taking agricultural machine total power in Heilongjiang province as original data, the three combination forecasting models were obtained through respectively using shapley value, information entropy...
From the current research review and defects of the present leakage prediction model analysis, a multivariate grey GM (0, N) (Multi-factor Grey Model) considering the water supply pressure, water supply volume and frequency of leakage was proposed, finally it was used to predict the leakage of water pipe network, the results showed that the precision of grey GM (0, N) model was greatly improved in...
In order to obtain the law of the building settlement and forecast it effectively, neural network model was established for building settlement forecasting based on measured data, and an engineering example is shown to test and verify. Firstly, data of building settlement measured were normalized; embedding dimension was selected to establish the leaning samples. Mean square error (MSE) and mean absolute...
A daily gas load forecasting method based on Support Vector Machine theory is developed in this paper. Some aspects of data preprocessing are discussed, such as normalization method, data grouping method and the period of history data using as input vector. Proper normalization method, which is to map gas load data from the small and narrow range to the big and wide, will improve the forecasting accuracy...
In this paper wavelet decomposition is combined with autoregressive models to analyze and predict climate time series such as solar radiation and photovoltaic cell temperature. The method is tested on climate parameters for analysis, and solar radiation and cell temperature for prediction. The work effectiveness is evaluated by the prediction of the electric energy produced by a 100 Wp photovoltaic...
In order to accurately predict mine gas flow volume, based on the theory of the gray model, analyses the GM(1,1) mechanism, and point out the model of theoretical defects, than do optimum to the model of time response function. Optimum model will use to mine gas flow volume of prediction, the prediction accuracy for 98.1784%. Results show that the optimum model for the mine gas flow volume prediction...
A method for prediction discrete data is presented in this article. In order to forecast the discrete data, the experiment that use the GM (1,1) and BP networks to predict discrete data are respectively executed, we found that AGO operation in the GM method can effectively reduce randomness of the discrete data, so AGO operation is applied into the BP network method. According to the result of the...
Spatial decision support system (SDSS) is based on geographic information system (GIS), and has the core model base. SDSS is a decisive system of facing objects space information and users which not only have analysis reserve and estimation for information, but also have information forecast and decision. This article has discussed complex product by using SDSS, and has designed the elementary model...
A method of forecasting process capability index was recommended based on least squares support vector machines (LS-SVM). The parameters of LS-SVM were optimized by Bayesian framework. The higher precision model of prediction for process capability index was built by optimizing parameters. The prediction results show it have many advantage, such as lower error and higher fitting, and it can be used...
Discrete Event Simulation (DES) has widely been used for mid and long term forecasting in wafer fabrication plants. But the use of DES for short term forecasting has been limited due to the perceived modelling and computation complexity as well as the non-steady state nature of today's wafer fab operations. In this paper, we discuss some important modelling issues associated with building an online...
As for the characteristics of the time-lag and periodic in the multiple sectional short-term traffic flow, this paper proposes a new Multi-variable triangle model through introducing delay factor τ and periodic factor p into MGM(1, N) model. In this article, we research modeling process, parameter estimation, precision inspection, forecasting and so on. During the modeling, we establish a optimal...
In this paper, the bullwhip effect in a two-stage supply chain with one supplier and two retailers is measured. The customer demand is assumed to be followed an AR(1) model and is forecasted at each retailer by using the minimum mean square error forecasting method. In addition, the retailers employ the base stock inventory policy. Among the findings of this research, it is interesting to note that...
In order to address the problems involved in low modeling efficiencies and complex programming design involved in implementing runoff forecasting using conventional modeling technologies, this paper presents a novel visual modeling system that integrates the visual modeling technology with the support vector machine (SVM) based model with the purpose of enhancing the flexibility of interactive and...
Employing the statistics of air cargo volume of China from 1997 to 2007, using the Gray GM(1, 1) model and Regression Analysis model combined for optimization, an air cargo volume combination forecasting model based on approximate optimal non-negative weight is established and validated. The result shows that this model is effective, suitable, more accurate, and is applicable to practice. Then the...
In view of the corrosion of cooling water system, the dynamic simulation test was conducted with the cooling water dynamic simulation experiment device. In the test period the corrosion rate and the water quality factors were monitored. Based on the test data, an intelligent prediction model of cooling water corrosion rate based on least squares support vector machine (LS-SVM) is constructed, in which...
This paper proposes a novel improved model for fuzzy time series forecasting using high-order and Type-2 fuzzy time series. Some Type-1 fuzzy time series model based on first order and high-order fuzzy time series have already been developed, however the accuracy of the forecasting values sill needs to be improved. In this paper, we present a high-order fuzzy time series model using Type-2 fuzzy sets...
In this study, a hybrid model using multivariate adaptive regression splines (MARS) and SVR is proposed for sales forecasting of information technology (IT) products. Support vector regression (SVR) has become a promising alternative for forecasting due to its generalization capability in obtaining a unique solution. However, one of the key problems is that SVR can not identify which forecasting variables...
Air pollution has been a huge problem for a long time, more and more scientists focus on this hot topic, In this paper we presented a series data analysis methods for Los Angeles Long Beach datasets by Seasonal ARIMA(autoregressive integrated moving average) model and MCMC(Markov chain Monte Carlo) method. The MCMC methods are studied with LA long beach air pollution PM 2.5 traffic from 1997 to 2008...
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