摘要
空调负荷是近年来增长较快的一类负荷,其特性对电网的电压稳定性影响很大。夏季影响空调负荷的因素主要是温度和湿度的变化。为了更好的预测空调降温负荷,研究了温度和湿度对空调负荷的影响。利用BP人工神经网络对电网空调负荷进行了预测,经过分析把日平均湿度量化成4段,和日平均湿度实际数值的模型进行计算比较,结果显示考虑日最高温度和日平均湿度量化为4段能更好的模拟温度、湿度和空调负荷之间的非线性关系,能更好的对电网空调负荷进行预测。
Air conditioner loads increase quickly in recent years.This kind of loads impacts on the voltage stability of power system greatly.The main factors influencing air conditioners load is the temperature and humidity changes.After recognizing that humidity can be divided into four different comfort levels,a multiple layer feed-forward neural network model is used to model the two dimensions impact of temperature and humidity.The results are compared to a numerically determined form.And four different comfort levels of humidity can give better representation of the nonlinear relationship between air conditioners load and weather conditions.
出处
《电力需求侧管理》
2010年第4期27-30,共4页
Power Demand Side Management
