人工神经网络在短期负荷预测中的应用-------外文翻译.doc
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人工神经网络在短期负荷预测中的应用-------外文翻译,abstract：we discuss the use of artificial neural networks to the short term forecasting of loads. in this system, there are two types of neural networks: non-li...
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Abstract：
We discuss the use of artificial neural networks to the short term forecasting of loads. In this system, there are two types of neural networks: non-linear and linear neural networks. The nonlinear neural network is used to capture the highly non-linear relation between the load and various input parameters. A neural networkbased ARMA model is mainly used to capture the load variation over a very short time period. Our system can achieve a good accuracy in short term load forecasting.
Key words: short-term load forecasting, artificial neural network
1、Introduction
Short term (hourly) load forecasting is an essential hction in electric power operations. Accurate shoirt term load forecasts are essential for efficient generation dispatch, unit commitment, demand side management, short term maintenance scheduling and other purposes. Improvements in the accuracy of short term load forecasts can result in significant financial savings for utilities and cogenerators.
Various teclmiques for power system load forecasting have been reported in literature. Those include: multiple linear regression, time series, general exponential smoothing, Kalman filtering, expert system, and artificial neural networks. Due to the
摘要：
在本文，我们将讨论如何利用人工神经网络对短期负荷进行预测。在这类系统中，有两种类型的神经网络：非线性和线性神经网络。非线性神经网络是用来捕获负荷和各种输入参数之间的高度非线性关系。基于ARMA模型的神经网络，主要用来捕捉很短的时间期限内负载的变化。我们的系统可以实现准确性高的短期负荷预测。
关键词：短期负荷预测，人工神经网络
1、绪论
短期（每小时）负荷预测对于电力系统的稳定运行是必要的。准确的负荷预测对于高效的发电调度，开停机计划，需求方的管理，短时维护安排或其他目的等是很必要的。改进短期负荷预测的准确性能为公共事业和联合发电节省很多开支。
很多种电力系统负荷预测方法在学术界已经报导了。这些方法包括：多元线性回归法，时间序列法，一般指数平滑法，卡尔曼滤波法，专家系统法和人工神经网络预测法。由于电力负荷和各种参数（天气的温度，湿度，风速等）之间的
We discuss the use of artificial neural networks to the short term forecasting of loads. In this system, there are two types of neural networks: non-linear and linear neural networks. The nonlinear neural network is used to capture the highly non-linear relation between the load and various input parameters. A neural networkbased ARMA model is mainly used to capture the load variation over a very short time period. Our system can achieve a good accuracy in short term load forecasting.
Key words: short-term load forecasting, artificial neural network
1、Introduction
Short term (hourly) load forecasting is an essential hction in electric power operations. Accurate shoirt term load forecasts are essential for efficient generation dispatch, unit commitment, demand side management, short term maintenance scheduling and other purposes. Improvements in the accuracy of short term load forecasts can result in significant financial savings for utilities and cogenerators.
Various teclmiques for power system load forecasting have been reported in literature. Those include: multiple linear regression, time series, general exponential smoothing, Kalman filtering, expert system, and artificial neural networks. Due to the
摘要：
在本文，我们将讨论如何利用人工神经网络对短期负荷进行预测。在这类系统中，有两种类型的神经网络：非线性和线性神经网络。非线性神经网络是用来捕获负荷和各种输入参数之间的高度非线性关系。基于ARMA模型的神经网络，主要用来捕捉很短的时间期限内负载的变化。我们的系统可以实现准确性高的短期负荷预测。
关键词：短期负荷预测，人工神经网络
1、绪论
短期（每小时）负荷预测对于电力系统的稳定运行是必要的。准确的负荷预测对于高效的发电调度，开停机计划，需求方的管理，短时维护安排或其他目的等是很必要的。改进短期负荷预测的准确性能为公共事业和联合发电节省很多开支。
很多种电力系统负荷预测方法在学术界已经报导了。这些方法包括：多元线性回归法，时间序列法，一般指数平滑法，卡尔曼滤波法，专家系统法和人工神经网络预测法。由于电力负荷和各种参数（天气的温度，湿度，风速等）之间的
