基于神经网络的短期负荷预测.docVIP

本科毕业设计（论文） 【摘 要】 电力用户是电力工业的服务对象，电力负荷的不断增长是电力工业发展的根据。正确地预测电力负荷，既是为了保证无条件供应国民经济各部门及人民生活以充足的电力的需要，也是电力工业自身健康发展的需要。 长期以来，国内外学着对电力系统负荷预报进行了广泛而深入的研究，提出了许多有效的方法。本文应用目前较为流行的神经网络方法对电力系统短期负荷进行预测，主要进行了以下工作： 了解电力系统短期负荷预报的现状，总结国内外的研究方法。 本文讨论了影响负荷的各种因素。建立了充分考虑各种因素的短期负荷预测模型，在输入变量中考虑了临近日负荷特点，以及各种气象因素，对输入负荷值进行归一化处理。 本文设计了基于BP神经网络并考虑节假日因素的短期负荷预测模型，有效提高了BP网络的收敛速度，并对气温数据进行归一化处理，考虑了气温对电力负荷的影响。 最后总结了利用matlab进行短期负荷预测的设计与实现，并比较了负荷的影响因素，阐述了电力系统短期负荷预测的发展前景。 关键词：神经网络；BP网络；短期负荷预测；matlab； 【Abstract】 The power user is the object of the electricity industry and the continuous growth of the electric load is the basis of the electric power industry development. Correctly predict the electricity load, the various departments of the national economy and peoples lives both in order to ensure the unconditional supply adequate power needs of the electric power industry of their own health development needs. From now on, researchers have set many efficient measures and the popular neural network measure will be used in this thesis for load forecasting in power system. The main work included: Looking into the present condition of power system short—term load forecasting and summarizing research method in the world． This thesis analyzes every kind of factor which impacts load，and sets up the load forecasting model that many factors are considered．In its input features，the load characteristic of near days and every kind of weather factors that considered．Then we unify the input variables，quantify the temperature． This thesis designs the load forecasting model consisting of variety of factors based on Back Propagation Network. It shortened training time and enhanced forecasting precision by considering the temperature factor and holidays factors. At last，summarizing the load forecasting work designed by matlab software，coming up with some improved proposals and introducing development possibility of short-term load forecasting of power system． Key Words: Neural Network; Back Propagation Network;