电气专业毕业设计外文翻译--基于人工智能的长期电力负荷预测-电气类(编辑修改稿)

电气专业毕业设计外文翻译--基于人工智能的长期电力负荷预测-电气类(编辑修改稿)内容摘要：

lve problems. Every neuron applies an input, activation and an output function to its input to calculate its output. The neural work is configured for a specific application, such as data classification or pattern recognition, through a learning process called “training”. The first researchers who introduced the ANN application in LTELF were Lee et al., who proposed an innovative ANN methodology for the LTELF problem. Park et al. proposed the use of a multilayer work with three layers, . one input, one hidden and one output. The training of the work was performed through a simple backpropagation algorithm. Using load and weather information, the system produced three different forecast variables. Lee et al. treated electric load demands as a nonstationary time series, and they modeled the load profile by a recurrent neural work. In 1992, Peng et al. presented a search procedure for selecting the training cases for ANNs to recognize the relationship between weather changes and load shape, while Ho et al. implemented a multilayer neural work with an adaptive learning algorithm. Chen et al. proposed an ANN for weather sensitive long term load forecasting, while an alternative technique using a recurrent high order neural work was considered by Kariniotakis et al. Papalexopoulos et al. proposed the inclusion of additional input variables, such as a seasonal factor and a cooling/heating degree into a single neural work. Czernichow et al. used a fully connected recurrent work for load forecasting in which the learning database consisted of 70,000 patterns with a high degree of diversity. Mandal et al. applied neural works for LTELF in which the inputs consisted of the past load data only, and no weather variables were used, while Sforna and Proverbio investigated the application of ANNs in LTELF, through a research project at ENEL, and confirmed their positive contribution. In 1997, Kiartzis et al. presented the Bayesian bined predictor, a probabilistically motivated predictor for LTELF based on the bination of an ANN predictor and two linear regression predictors. The method was applied to LTELF for the Greek Public Power Corporation dispatching center of the island of Crete. Ramanathan et al. made several parisons of statistical, time series and ANN methods for the LTELF. In 1998, Sforna reported the implementation of a software tool, called NEUFOR, based on ANN technology and specifically designed to meet the operational needs of utility power system dispatchers regarding online operation, while Papadakis et al. continued to improve their previous work. The same goes for Drezga and Rahman. The development of improved neural work based LTELF mod。