结合支持向量机和马尔可夫链算法的中长期电力负荷预测模型

中长期电力负荷预测具有可利用的历史数据较少和受外界不确定性因素影响较大的特点,传统的单一预测模型很难满足生产实际的需要。在简要分析了支持向量机和马尔可夫链各自优势的基础上,提出了一种基于支持向量机和马尔可夫链的组合负荷预测模型。通过经改进的粒子群算法优化的支持向量机对历史负荷序列进行粗预测,接着借助马尔可夫链确定负荷序列的状态转移概率矩阵,通过划分系统状态以及分析实际值与支持向量机拟合值的相对误差,得到最终的预测结果。实际算例验证了该模型的有效性和优越性。

A Forecasting Model of Medium / Long Term Power Load in Combination of the Support Vector Machine and Markov Chain Algorithms

In processing medium and long term power load forecasting, with less available historical data, and many uncertainties factors are influencing the results, thus the traditional single forecast model is difficult to meet actual production needs. On the basis of brief analysis of the advantages of Support Vector Machines and Markov Chain model, a new combination prediction model is put forward based on Support Vector Machines and Markov Chain theory. Support Vector Machines optimized by improved particle swarm algorithm was used to forecast the sequence of historical load; The state divert probability matrix of the load time series is gotten by Markov chain; The final results is determined by division of system state and analysis of relative error value between actual values and support vector machines predict values. Practical example reveals the validity and advantage of the proposed model.