Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm
Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm
复制标题
使用人工免疫系统学习算法训练的人工神经网络进行短期负荷预测
DOI：
10.1109/uksim.2010.82
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
T. Rahman
中科院分区：
文献类型：
--
作者：
M. Hamid;T. Rahman
负荷预测对电力企业的运行是非常重要的。它是实现电力经济调度的前提条件，在保证电力系统可靠运行的同时，还能提高效率。电能需求高度依赖于各种独立变量，如天气、温度、节假日和一周中的几天。预测的准确性对于确保在不影响电力系统运行的经济方面的情况下向用户提供持续的电力供应至关重要。针对短期负荷预测模型，提出了一种基于人工免疫系统(AIS)学习算法训练的人工神经网络。利用两组电能需求数据对该算法的性能进行了测试。结果表明，所提出的AIS学习算法能够提供与以BP为学习算法的人工神经网络相当的预测能力。因此，这表明人工免疫系统可以作为人工神经网络的一种替代学习算法来实现。
Load forecasting is very essential to the operation of electric utility. It is a pre-requisite to economic dispatch of electrical power and enhances the efficiency besides ensuring reliable operation of a power system. Electrical energy demand is highly dependent on various independent variables such as the weather, temperature, holidays, and days in a week. The accuracy of the forecast is important to ensure consistent electrical power supply to customer without compromising the economic aspect of the power system operation. In this paper, an Artificial Neural Network (ANN) trained by the Artificial Immune System (AIS) learning algorithm is proposed for short term load forecasting model. Two sets of electrical energy demand data were used to test the capability of the proposed algorithm. Based on the results obtained, it shows that the proposed AIS learning algorithm is capable to provide a comparable forecast to that of Artificial Neural Network with Back Propagation (BP) as the learning algorithm. Hence, this indicates that Artificial Immune System could be implemented as an alternative learning algorithm for an Artificial Neural Network.