基于神经网络的热负荷预测模型研究

Research on heat load prediction model based on neural network

中文关键词: 大数据分析  用热特性  BP神经网络模型  小波神经网络模型

英文关键词: big data analytics  thermal characterization  BP neural network models  wavelet neural network models

中文摘要:

      随着计算机技术的发展，大多数热电公司已经建立了平稳运行的网络系统，这些网络系统在运营管理工作中起到了关键作用。对于网络系统中形成的大量数据，怎样合理分析数据来更好地为企业服务已成为目前广受关注的问题。为了解决上述问题，本文基于大数据分析热用户的用热特点，并以某热电厂的大量历史数据为例，建立BP神经网络预测模型，预测热电厂蒸汽负荷。针对传统BP神经网络模型容易陷入局部最优解的问题，本文将小波理论与传统BP神经网络模型相结合，构建小波神经网络模型，提高对热电厂蒸汽负荷预测的准确度。

英文摘要:

      With the development of computer technology, most of the thermoelectric companies had established smooth running network systems, which played a key role in operation and management. For the large amount of data formed in the network system, how to reasonably analyze the data to better serve the enterprise has become a widely concerned issue. In order to solve the above problems, this paper analyzes the heat characteristics of heat users based on big data, and takes a large amount of historical data of a thermal power plant as an example to establish a BP neural network prediction model to predict the steam load of the thermal power plant. Aiming at the problem that the traditional BP neural network model is easy to fall into the local optimal solution, this paper combines the wavelet theory with the traditional BP neural network model to construct a wavelet neural network model to improve the accuracy of steam load prediction for thermal power plants.