【作者】 张晓；

【导师】 敬东；

【作者基本信息】 四川大学 ， 电力系统及其自动化， 2001， 硕士

【摘要】 电力系统短期负荷预测工作一直倍受关注，是电力部门的一项重要工作。短期负荷预测关系到电力系统的调度运行和生产计划，准确的负荷预测有助于提高系统的安全性和稳定性，能够减少发电成本。随着电力市场的建立和发展，短期负荷预测将发挥越来越重要的作用。 本文在探讨了电力系统负荷的组成、特点，并分析比较了常用的预测方法优缺点的基础之上，采用人工神经网络与模糊逻辑相结合的方法建立了负荷预测模型，把短期负荷预测工作分为两部分：即基本负荷分量和温度、节假日负荷分量。在人工神经网络部分完成基本负荷分量的预测工作，在此不考虑温度、节假日对负荷变化的影响，减小了神经网络的工作量，并简化了网络结构；在模糊逻辑部分完成对基本负荷分量的修正，仅仅考虑影响负荷变化的温度、节假日等情况，把“模糊语言变量”引入预测系统，达到了“语言变量”与“数字变量”的统一，使专家经验得到充分的利用，得到最终的负荷预测值。 本文研究的短期负荷预测算法简单：通过引进平滑系数和遗忘系数，提高了人工神经网络的学习速度，即快速人工神经网络；在模糊逻辑中，充分利用了人们对负荷变化取得的主观经验，引进不平均隶属函数，来反映负荷对温度的敏感性。 在本文的最后，提出了在电力市场形势下的短期负荷预测工作应该改进的方面，以及为完善电力市场而应该做的准备工作。更多还原

【Abstract】 Great attention is always paid to power system short-term load forecasting (STLF), which is an important task of power utilities. STLF is concerned to the dispatching work and production scheme of power system, and accurate load forecasting is helpful to the security and stability of power system as well as saving its generation costs. With the establishment and development of the power market, STLF will play more and more important role in power system.After discussing the constituents and characteristics of the electric load, analyzing and comparing the merits and shortcomings of some forecasting methods, the paper chooses the hybrid method, artificial neural network (ANN) and fuzzy logic, to build the load forecasting model, which separates the forecasting job into two parts: one is the base load component, and the other is the revision for temperature and holidays. The based load (BL) component is forecasted in ANN, no considering the effect of temperature or holidays, which reduces the work for ANN and simplifies its structure; the BL revision is finished in Fuzzy, only considering the influence of temperature and holidays, where uses the expert experiences by introducing fuzzy language variable and gets the final forecasted load.The paper presents a simple algorithm for STLF: the learning speed of ANN is improved by using smoothing factor and forgetting factor, that is the fast ANN; the subjective experiences is well used in Fuzzy Logic, where the uneven membership function is introduced, reflecting load sensitivity to temperature.In the end, the paper discusses the improvement of STLF for the new situation under power market, puts forward the ready work to better power market.更多还原