[1]王 琦,胡 磊,杨超杰.改进型神经网络的热负荷预测[J].工业仪表与自动化装置,2020,(06):11-16.[doi:1000-0682(2020)06-0000-00]
　WANG Qi,HU Lei,YANG Chaojie.Heat load forecast of improved neural network[J].Industrial Instrumentation & Automation,2020,(06):11-16.[doi:1000-0682(2020)06-0000-00]
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改进型神经网络的热负荷预测

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2020年06期
页码:
11-16
栏目:
出版日期:
2020-12-15

文章信息/Info

Title:
Heat load forecast of improved neural network
作者:
王 琦; 胡 磊; 杨超杰
山西大学 自动化系，山西 太原 030013
Author(s):
WANG Qi;  HU Lei; YANG Chaojie
Department of Automation, Shanxi University, Shanxi Taiyuan 030013,China
关键词:
热负荷预测; BP神经网络; 改进型神经网络; 预测精度
Keywords:
thermal load forecasting;  BP neural network;  improved neural network;  prediction accuracy
分类号:
TU832
DOI:
1000-0682(2020)06-0000-00
文献标志码:
A
摘要:
在城市集中供暖方面，热电厂的短期热负荷预测对提高热电厂的经济效益和热能利用率十分重要。该文以山西某热电厂的供热系统的换热站作为研究对象，使用遗传算法和粒子群算法改进BP神经网络，基于热负荷相关的历史数据构建改进型神经网络的热负荷预测系统。仿真结果显示，BP神经网络预测系统的波动程度比较大，预测精度低，而改进型的神经网络算法克服了这些缺点，在历史样本数据较少的情况下，仍然保持很高的预测精度，改进后的预测系统精度较高、稳定性较强，满足工业生产需求。
Abstract:
In terms of urban central heating, the short-term heat load prediction of thermal power plants is very important to improve the economic efficiency and thermal energy utilization rate of thermal power plants. This paper takes the heat exchange station of the heating system of a thermal power plant in Shanxi as the research object, uses genetic algorithm and particle swarm optimization to improve the BP neural network, and builds an improved neural network thermal load prediction system based on the historical data of thermal load. The simulation results show that the BP neural network prediction system has a relatively large degree of fluctuation and low prediction accuracy, and the improved neural network algorithm overcomes these shortcomings. In the case of less historical sample data, it still maintains high prediction accuracy and improvement. The prediction system afterwards has high precision and strong stability, and meets the needs of industrial production.

参考文献/References:

[1] DING Yan, ZHANG Qiang, YUAN Tianhao, et al. Model input selection for building heating load prediction: A case study for an office building in Tianjin[J]. Energy & Buildings,2018,159:254-270.

[2] 景胜蓝，王飞，雷勇刚.热负荷预测方法研究综述[J].建筑热能通风空调，2015,34(04):31-35.
[3] 于晓娟,齐先硕,顾吉浩,等.基于混合算法优化支持向量机的供热负荷预测模型[J].河北工业大学学报,2019,48(05):39-46.
[4] PARK Sang Ku, MOON HyeunUN Jun, MIN Kyung Chon,et al. Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system[J]. Energy & Buildings,2018,165: 206-215.?div>[5] FANG Tingting,Risto Lahdelma. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system[J]. Applied Energy,2016,179: 544-552.?div>[6] 孙育英,王伟,朱佳鹤,盖轶静,李林涛.空调运行负荷的混沌特性分析及GRNN混沌预测方法研究[J].建筑科学,2014,30(10):13-17+40.?div>[7] XIE Ling. The Heat load Prediction Model based on BP Neural Network-markov Model[J]. Procedia Computer Science,2017,107: 296-300.?/div>
[8] 刘倩颖，阮应君，时翔，等.基于kmeans聚类与BP神经网络算法的办公建筑逐时电负荷预测[J].热能动力工程，2018,33(3):138-144.
[9] Jovanovi Radia , Sretenovi Aleksandra A, ivkovi Branislav D. Ensemble of various neural networks for prediction of heating energy consumption[J]. Energy & Buildings,2015,94: 189-199.
[10] 刘艺，张琨．基于小波去噪和GA-Elman神经网络的短时交通流预测［J］．交通科技与经济，2017，92(06) :80-85．
[11] 姜平,赵保国,张海伟,等.基于T-S模糊神经网络的热负荷预测模型[J].自动化仪表,2019,40(11):20-23.
[12] 王冰冰,赵天乐.基于遗传算法改进BP神经网络的风电功率预测研究[J].电工电气,2019(12):16-21.
[13] 崔东文，郭荣.BP神经网络模型与灰色GM（1，1）模型在需水预测中的应用[J].水资源研究，2012，33（03）:19-22.
[14] KASSA Yordanos, ZHANG J H, ZHENG D H, et al. A GA-BP hybrid algorithm based ANN model for wind power prediction[C]//4th IEEE International Conference on Smart Energy Grid Engineering (SEGE). 2016:158-163.
[15] 李松，罗勇，张铭锐. 遗传算法优化BP神经网络的混沌时间序列预测[J].计算机计算工程与应用，2011,47（29）:52-55.
[16] YANG Xiaobin. The early warning research of enterprise financial crisis based on BP neural network[J].International Journal of u- and e-Service,2016,9(9).
[17] 程军,李荣钧.基于粒子群优化的神经网络预测模型[J].数学的实践与认识, 2015,45(03):176-180.

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备注/Memo

备注/Memo:
收稿日期：2020-05-31
作者简介：王琦（1973），女，河北定州人，教授。E-mail:15702420745@163.com
更新日期/Last Update: 1900-01-01