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. 1. Introduction Chaos theory is the important component of the nonlinear science [1]. It is the random phenomena which appeared in the deterministic nonlinear dynamic system. Chaos is not a disorder but has a delicate inner structure. It reveals the order and regularity hidden behind the disordered and complex phenomena. Since the 90s, chaos theory has been well developed. Many subjects are infiltrated and promoted [2] under this tendency. So the research on chaos gets an access to a breakthrough. At the meanwhile the application about chaos theory gets a widely growing. Short-term power load forecasting is a multidimensional nonlinear system. It is easy to get the load time series in power system. But these data are nonlinear and difficult to establish a matched mathematical model to forecast the next-hour load. Recently, more and more nonlinear time series forecasting models based on chaos theory [3, 4] are applied to power short-term load forecasting. And they have achieved good prediction results. So chaos theory is employed to analyze the characters of the load time series and applied into the power system forecasting in this paper. There are many models to be adopted into power system load forecasting. They can generally be summarized as follows: time series model, regression model, expert system model, grey theory model, and fuzzy logic model. But according to chaotic characters of the load time series, ANN [5–9] is established and applied into the power system well. In view of neural network parallel processing and powerful nonlinear mapping ability, chaotic time series can be learned for unknown dynamic system and then predicted and controlled. As chaotic time series have an inner deterministic regularity which stems from nonlinearity, it represents the relevance of the time series on time delay state space. The feature makes the system have some kind of memory capacity. However it is difficult to express the
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