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ANN using BP is widely used in power load forecasting. But there are some existed problem of the BP algorithm: (1) Convergence speed is slow, usually convergence needs more than one thousand times; (2) Objective function is prone to getting into local minimum.. How to overcome the shortcoming that convergence speed is slow and network is prone to trapping in local minimum has not been resolved. Training...
First, the forecasting principle and improved algorithms about BP ANN are briefly introduced. Then, an improved algorithm about BP ANN is put forward which based on subordinating degree function, and conduct simulating tests. The result indicates that convergence is rapid without changing the forecasting precision. Based on this and combined with the characteristic of power load forecasting, a model...
Against these disadvantages like local minimum, slow convergence speed, non-convergence and difficulty in obtaining of global optimal point, the new Neural Networks with Weight Function is proposed in this paper with simple network topology constituted by input layer and output layer only. This network is used in establishing the Energy Consumption Forecasting Model of DAGUSHAN Ore Dressing Plant...
This paper presents a new BP neural network (BP NN) forecast model named IPSO-BP forecast model that is based on an improved particle swarm optimization (IPSO). The improved PSO employs parameter with crossover operator and mutations operator to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. This study uses the IPSO algorithm...
Accurate forecasting of short-term electricity load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Artificial neural network is a novel type of learning method, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, it is proposed a new optimal model...
This paper presents a new approach for short term load forecasting using a diagonal recurrent neural network with an adaptive learning rate. The fully connected recurrent neural network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time. The DRNN is a modified model of FRNN. It requires fewer weights than FRNN and rapid convergence has been...
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