1、PDF外文：http:/ 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
2、 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
3、 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
4、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 i
5、n 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
6、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 (
7、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 expected to run this system at dai
8、ly 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-linear relation betw
9、een 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 short time period. &nb
10、sp; 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 the non-linear neural network
11、. 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
12、 data provided 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 descri
13、bes the variables sigdicantly affecting short term load forecasting； in Section III, we present 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,
14、 some simulation result is given； finally, we describe the enhancement to our system in Section VII. 2、 Variables Afferting Short-Term Load Some of the variables affecting short-term electxical load are: Temperature Humidity Wind speed Cloud cover Length of daylight Geographical region Holida
15、ys Economic factors Clearly, the impacts of these 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 l
16、oad due to weather conditions than regions with 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 day
