| 基于改进BP神经网络的供热负荷预测模型 |
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| 引用本文： | 李思琦,蒋志坚.基于改进BP神经网络的供热负荷预测模型[J].教育技术导刊,2019,18(7):41-44. |
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| 作者姓名： | 李思琦 蒋志坚 |
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| 作者单位： | 北京建筑大学 电气与信息工程学院，北京 100044 |
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| 基金项目： | 住房和城乡建设部科技项目（2011-k8-4） |
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| 摘 要： | 为在自然环境条件下对供热负荷进行较为准确的预测，分析了对供热负荷产生影响的自然因素，利用回归分析法建立负荷预测模型。在误差较大情况下提出利用神经网络法建模，采用差分进化算法对神经网络的阈值和权值进行优化。使用经过优化的神经网络进行负荷预测，在MATLAB环境下进行仿真。仿真结果表明，采用该方法可得到更为准确的供热负荷预测模型，对供热站节能运行有一定意义。
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| 关 键 词： | 神经网络 非线性系统 负荷预测 供热负荷 节能 |
| 收稿时间： | 2018-10-23 |
Forecasting Model of Heating Load Based on Improved BP Neural Network |
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| LI Si-qi,JIANG Zhi-jian.Forecasting Model of Heating Load Based on Improved BP Neural Network[J].Introduction of Educational Technology,2019,18(7):41-44. |
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| Authors: | LI Si-qi JIANG Zhi-jian |
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| Institution: | School of Electrical and Information Engineering，Beijing University of Civil Engineering and Architecture，Beijing 100044，China |
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| Abstract: | Because the heating load can not be predicted accurately during the operation of the heating station， a large amount of energy has been wasted. In order to predict the heating load accurately according to the natural environment， this paper analyzes the natural factors which can affect the heating load. A load forecasting model is established by regression analysis. On the basis of the large error of the regression model， the neural network method is used to establish the heating load model， and the threshold and weight of the neural network are optimized by differential evolution algorithm. The optimized neural network is used to forecast the load， and the simulation is carried out under the environment of MATLAB. According to the simulation results， a more accurate heating load forecasting model can be obtained by using this method. It has certain significance for energy saving operation of heating station. |
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| Keywords: | neural network nonlinear system load forecasting heating load energy saving |
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