本文为印度Rourkela国家技术研究所（作者：MANOJ KUMAR）的学士论文，共48页。

本文将人工神经网络方法应用于大型电力系统的短期负荷预测。负荷有两种不同的模式：工作日和周末模式。周末模式包括星期六、星期日和星期一负荷。提出了一种非线性负荷模型，并对几种用于短期预测的神经网络结构进行了测试。神经网络的输入是历史记录的负荷数据，而神经网络的输出是对某天的负荷预测。对具有一个或两个隐藏层的网络进行了不同神经元组合的测试，并从预测误差的角度对结果进行了比较。当神经网络被分为不同的负荷模式时，能给出很好的负荷预测。

本项目利用人工神经网路进行短期内的每小时负荷预测。为了证明该方法的有效性，澳大利亚国家电力市场（NEMMCO）网站的公开数据被用来预测维多利亚电力系统的每小时负荷。我们预测了一周内的每小时负荷需求，具有很高的准确性。从NEMMCO网站上获取的2006年历史负荷数据被分为几个部分，其中一半用于训练，另一半用于测试人工神经网络。

神经网络使用的输入是全天（24小时）的每小时负荷需求，以及两个主要城市的每日温度、湿度和风速；获得的输出是第二天的每小时负荷需求预测数据。设计的神经网络分为三层：输入层、隐藏层和输出层。输入层包括37个节点，而隐藏层神经元的数量因网络性能的不同而不同，输出层有24个神经元。我们对网络进行了6周的训练，经过训练的网络在某一周的数据上进行测试，得到了2.64%的绝对平均误差。

Artificial Neural Network (ANN) Method is applied to fore cast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturdays, Sunday and Monday loads. A nonlinear load model is proposed and several structures of ANN for short term forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives good load forecast. This project presents a study of short-term hourly load forecasting using Artificial Neural Networks (ANNs). To demonstrate the effectiveness of the proposed approach, publicly available data from the Australian national electricity market (NEMMCO) web site has been taken to forecast the hourly load for the Victorian power system. We predicted the hourly load demand for a full week with a high degree of accuracy. Historical load data of 2006 obtained from the NEMMCO web site was divided into several where half of them are used for training and the other half is used for testing the ANN. The inputs used were the hourly load demand for the full day (24 hours) for the state and the daily temperature, humidity and wind speeds of two major cities. The outputs obtained were the predicted hourly load demand for the next day. The neural network used has 3 layers: an input, a hidden, and an output layer. The number of inputs was 37 while the number of hidden layer neurons was varied for different performance of the network. The output layer has 24 neurons. We trained the network over 6 weeks. An absolute mean error of 2.64% was achieved when the trained network was tested on one week‟s data.

1 人工神经网络简介

2 人工神经网络的结构

3 后向传播算法

4 函数近似

5 利用人工神经网络进行负荷预测

6 结论 

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