基于马尔可夫链和模糊聚类的电力系统短期负荷预测

提出一种马尔可夫链和模糊聚类相结合的预测方法,针对时间序列中出现的各种随机现象,分别建立数学模型.对样本所属状态采用模糊划分,使分类更符合实际情况;利用马尔可夫链对研究对象做状态分析,根据状态转移进行预测.该方法在电力系统负荷预测中使用,提高了算法的全局最优性能.在时间序列呈现较强的随机性时,本算法具有明显的优越性.仿真结果表明,对于各种扰动因素,预测误差可控制在3.5%以内.

Power System Short Term Load Forecasting Based upon a Combination of Markov Chain and Fuzzy Clustering

A new method based on the combination of fuzzy clustering and Markov chain models is presented. To different types of random phenomena in time series, several functions are built up respectively. State analysis of an object is carried out using the Markov chain, while fuzzy clustering is employed to the states of samples to suit the real case. Then according to state transfer, the load change is predicted. The new algorithm which is used in load forecasting reaches the global optimum, when the time series have strongly properties of randomness, the algorithm works well. Simulation results show that the error can be limited to the level of 3.5%.