【正文】 外文文獻(xiàn)原文 : Artificial Neural Networks in Short Term load Forecasting . Reinschmidt, President B. Ling Stone h Webster Advanced Systems Development Services, Inc. 245 Summer Street Boston, U 0221 0 Phone: 6175891 84 1 Abstract： We discuss the use of artificial neural works to the short term forecasting of loads. In this system, there are two types of neural works: nonlinear and linear neural works. The nonlinear neural work is used to capture the highly nonlinear relation between the load and various input parameters. A neural workbased 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: shortterm load forecasting, artificial neural work 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 mitment, 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 works. Due to the highly nonlinear relations between power load and various parameters (whether temperature, humidity, wind speed, etc.), nonlinear techniques, both for modeling and forecasting, tend to play major roles in the power load forecasting. The artificial neural work (A) represents one of those potential nonlinear techniques. However, the neural works used in load forecasting tend to be large in size due to the plexity of the system. Therefore, training of such a large bees a major issue since the end user is expected to run this system at daily or even hourly basis. In this paper, we consider a hybrid neural work based load forecasting system. In this work, there are two types of neural works: nonlinear and linear neural works. The nonlinear neural work is used to capture the highly nonlinear relation between the load and various input parameters such as historical load values, weather temperature, relative humidity, etc. We use the linear neural work to generate an ARMA model. This neural work based ARMA model will be mainly used to capture the load variation over a very short time period. The final load forecasting system is a bination of both neural works. 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 nonlinear neural work. We use WidrowHoff algorithm to train the linear neural our work structure is simple, the overall system training is very fast. To illustrate the performance of this neural workbased load forecasting system in real situations, we apply the system to actual demand data provided by one utility. Three years of hourly data (1989, 1990 and 1991) are used to train the neural works. The hourly demand data for 1992 are used to test the overall system. This paper is anized as follows: Section I is the introduction of this paper。 Section I1 describes the variables sigdicantly affecting short term load forecasting。 in Section III, we present the hybrid neural work used in our system。 in Section IV, we describe the way to find the initial work structure。 we introduce our load forecasting system in details in Section V。 and in Section VI, some simulation result is given。 finally, we describe the enhancement to our system in Section VII. Variables Afferting ShortTerm Load Some of the variables affecting shortterm electxical load are: Temperature Humidity Wind speed Cloud cover Length of daylight Geographical region Holidays 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 mercial loads than on industrial load. Regions with relatively high residential loads will have higher variations in shortterm load 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 shows the loadvariation over one day, starting at midnight. Figure Example of load variation during one day Hybrid Neurak Networks Our shortterm load forecasting system consists of two types of works:linear neural work ARMA model and feedforward .Nonlinear neural nonlinear neural work is used to capture the highly nonlinear relation between the load and various input use the linear neural work to generate an ARMA model which will be mainly used to capture the load variation over a very short time period(one hour). Linear Neutal Networks The general multivariate linear model of or