基于混沌因子及相空间重构后的神经网络短期电价预测的研究

影响电价因素众多，但在现实中不可能获得所有信息的资料，在这种信息不完全的情况下，为了更好地提高电价预测精度，通过分析电价和负荷时间序列的混沌性，用C-C方法分别重构其相空间，揭示出其本身蕴涵的规律，并采用数据挖掘技术中的相似搜索技术，挖掘出与预测日变化规律最相似的时间序列作为样本，利用BP神经网络这一具有高度自学习自适应能力的网络，拟合电价序列的重构函数。利用美国PJM电力市场的实际数据进行了实例预测，结果显示出良好的预测精度，并比传统BP网络能更好地预测休息日电价。

Short-term price forecasting based on chaotic property and phase space recostructed neural networks

There are many factors which can affect the electricity prices, but in fact, we can not get all of the information of these factors. In this state, to improve the predictive precision, this paper analyzes the chaotic property of the price and load time series and reconstructs the attractors using C-C theory. Besides, the similarity search technique in data mining is adopted to find the most similar time series as training date. Then BP neural network which has high ability to self learning and self adapting is used to find the reconstructive function. The actual data of American PJM power market is forecasted based on the theory above. The results have good predictive precision, especially in rest days.