Application of chaos and neural network in power load forecasting. (English) Zbl 1245.93100
Summary: This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of Artificial Neural Network (ANN). Improved Back Propagation (BP) algorithm based on Genetic Algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model.
MSC:
| 93C95 | Application models in control theory |
| 93C15 | Control/observation systems governed by ordinary differential equations |
| 34H10 | Chaos control for problems involving ordinary differential equations |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
Keywords:
power load forecasting; chaotic systems; Lyapunov exponents; artificial neural network (ANN); back propagation (BP) algorithm; genetic algorithm (GA)References:
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