《外文翻译--人工神经网络在短期负荷预测中的应用（适用于外文翻译+中英文对照）》-毕业论文.docVIP

PAGE 1 外文文献原文: Artificial Neural Networks in Short Term load Forecasting K.F. Reinschmidt, President B. Ling Stone h Webster Advanced Systems Development Services, Inc. 245 Summer Street Boston, U 0221 0 Phone: 617-589-1 84 1 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 highly nonlinear relations between power load and various parameters (whether temperature, humidity, wind speed, etc.), non-linear techniques, both for modeling and forecasting, tend to play major roles in the power load forecasting. The artificial neural network (A) represents one of those potential non-linear techniques. However, the neural networks used in load forecasting tend to be large in size due to the complexity of the system. Therefore, training of such a large net becomes a major issue since the end user is ex