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According to the characteristics of grey theory, G-P algorithm of phase space reconstruction and artificial neural network (ANN), a combined algorithm (G-G-NN) is proposed. The original time series is transformed by accumulated generating of grey prediction and G-P algorithm of phase space reconstruction. When a regular time series phase space is generated, neural network is adopted to forecast. The...
The power load forecasting precision being influenced by many factors, the traditional forecasting tools are not very taking the role. In fact, BP network has the characteristics of the applicable and self-learning, and grey method has the growth characteristics,this paper used the correcting coefficient to improved the grey method, so the grey BP network method can better reflect the increasing and...
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...
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...
To improve the accuracy of power load forecasting, this paper analyzes the defects as well as merits of artificial neural network (ANN) and grey prediction method, and it combines the two methods to propose a novel forecasting method called grey neural network (GNN). GNN utilizes the accumulation generation operation (AGO) of grey prediction to transform the original load data to first order AGO data...
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