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1、 模式识别大作业外文翻译Artificial Neural Networks in Short Term load Forecasting人工神经网络在短期负荷预测中的应用姓 名： 刘德龙 学 号： 03081413 班 级： 030814 日 期： 2011.05 外文文献原文:Artificial Neural Networks in Short Term load ForecastingK.F. Reinschmidt, President B. LingStone h Webster Advanced Systems Development Services, Inc.245 Summ
2、er Street Boston, U 0221 0Phone: 617-589-1 84 1Abstract：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 n
3、on-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 neur
4、al network1、IntroductionShort 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. Improv
5、ements 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,
6、 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 fo
7、recasting. 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 i
8、s expected to run this system at daily or even hourly basis.In this paper, we consider a hybrid neural network based load forecasting system. In this network, there are two types of neural networks: non-linear and linear neural networks. The nonlinear neural network is used to capture the highly non
9、-linear relation between the load and various input parameters such as historical load values, weather temperature, relative humidity, etc. We use the linear neural network to generate an ARMA model. This neural network based ARMA model will be mainly used to capture the load variation over a very s
10、hort time period.The final load forecasting system is a combination of both neural networks. To train them, sigxuiicant amount of historical data are used to minimize MAPE (Mean Absolute Percentage Error). A modified back propagation learning algorithm is carried out to train thenon-linear neural ne
11、twork. We use Widrow-Hoff algorithm to train the linear neural network.Since our network structure is simple, the overall system training is very fast.To illustrate the performance of this neural network-based load forecasting system in real situations, we apply the system to actual demand data prov
12、ided by one utility. Three years of hourly data (1989, 1990 and 1991) are used to train the neural networks. The hourly demand data for 1992 are used to test the overall system.This paper is organized as follows: Section I is the introduction of this paper; Section I1 describes the variables sigdica
13、ntly affecting short term load forecasting; in Section III, wepresent the hybrid neural network used in our system; in Section IV, we describe the way to find the initial network structure; we introduce our load forecasting system in details in Section V; and in Section VI, some simulation result is
14、 given; finally, we describe the enhancement to our system in Section VII.2、Variables Afferting Short-Term LoadSome of the variables affecting short-term electxical load are:TemperatureHumidityWind speedCloud coverLength of daylightGeographical regionHolidaysEconomic factorsClearly, the impacts of t
15、hese variables depend on the type of load: variations in temperature, for example, have a larger effect on residential and commercial loads than on industrial load. Regions with relatively high residential loads will have higher variations in short-term load due to weather conditions than regions wi
16、th relatively high industrial loads. Industrial regions, however, will have a greater variation due to economic factors, such as holidays.As an example, Figure 2.1 shows the loadvariation over one day, starting at midnight.Figure 2.1 Example of load variation during one day3、Hybrid Neurak NetworksOur short-term load forecasting system consists of two types of networks:linear neural network ARMA model and feedforward .Non-linear neu
