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With the logistics outsourcing more widely used, a growing number of third-party logistics (TPL) were introduced to the VMI (Vendor-managed Inventory) implementation strategy, in this mode, how to effectively integrate the logistics and information flow in the supply chain has become an important research topic. Due to the complexity and a certain randomness of this mode, this paper uses the generalized...
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...
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...
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...
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...
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...
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 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...
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