基于马尔科夫状态转移的家居负荷预测模型

智能配电网的发展增强了家居负荷预测的重要性.基于状态转移的研究思路,提出基于相似日选择的蒙特卡洛马尔科夫单个设备负荷预测模型,采用自下而上的分析方法,获取单个家庭的综合负荷水平.对温控类型设备,采用皮尔逊相关系数研究了环境温度与设备运行周期之间的相关性,建立了隐马尔科夫模型,依据当天外界环境信息对温控类型设备的压缩机运行状态做出预测,进一步计算了不同时间段内的平均功率体现用户负荷水平.仿真结果表明,基于相似日选取的蒙特卡洛马尔科夫模型对不同设备的日平均负荷的预测误差约为2％～8％,而隐马尔科夫模型对温控类设备状态预测的精度约为70％.

Household load forecasting model based on Markov state transition

The development of smart grid improves the em- phasis on family load forecasting. Based on the theory of state transition, a Monte Carlo Markov Chain load forecasting model of single equipment based on the selection of similar days is proposed,and the bottom - up analysis method to obtain the comprehensive load level of a single family is used. For the temperature control equipment, Pearson correlation coefficient is used to study the correlation between the ambient temperature and the operation cycle of the equipment, and the hidden Markov model is used to predict the operation state of the compressor of the temperature control equipment according to the external environment information of the day. With the operation state predicted, the average power in different time periods is calculated to reflect the user load level.The simulation results show that the predicted error of the Monte Carlo Markov chain model based on the similar day selection is about 2% ~8% for the daily load expectation of different equipment, while the predicted accuracy of the hidden Markov model for the temperature control equipment is about 70%.