Advertisement

The Elman Network of Heat Load Forecast Based on the Temperature and Sunlight Factor

  • Qi Li
  • Shiqi JiangEmail author
  • Xudan Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

In urban district heating systems, the change of heat load is greatly influenced by various exterior factors. In order to meet the demand of heating system while achieving energy conservation and environmental protection, it is in this study, many kinds of artificial neural networks are compared, and a kind of Elman neural network is proposed for modeling heat load forecasting based on the temperature and the sunlight factor. The method obtains the real-time weather temperature from the Application Programming Interface (API) interface of the meteorological web site, added the illumination intensity as an input of the heat load forecasting model, and established the sample data sequence of the forecasting model. The real-time data is used to update history data and it makes up the new inputs to achieve short-term heat load rolling forecasts. The simulation results show that this method can accurately predict the future heat load, and achieve the purpose of on-demand heating, energy conservation, and environmental protection.

Keywords

Heat load forecast Neural network Elman Weather forecast Sunlight factor 

Notes

Acknowledgements

The work was supported by the National Natural Science Foundation of China (61463040).

References

  1. 1.
    Sakawa, M., Kato, K., Ushiro, S.: Cooling load prediction in a district heating and cooling system through simplified robust filter and multilayered neural network. Appl. Artif. Intel. 15(7), 633–643 (2001)CrossRefGoogle Scholar
  2. 2.
    Yang, W.D., Wang, J.Z., Wang, R.: Research and application of a novel hybrid model based on data selection and artificial intelligence algorithm for short term load forecasting. Entropy 19(2), 52 (2017)CrossRefGoogle Scholar
  3. 3.
    Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., Johansson, C.: Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods. Energy 157, 141–149 (2018)CrossRefGoogle Scholar
  4. 4.
    Dou, C.X., Qi, H., Luo, W., Zhang, Y.M.: Elman neural network based short-term photovoltaic power forecasting using association rules and kernel principal component analysis. J. Renew. Sustain. Energy 10 (2018)Google Scholar
  5. 5.
    Zhang, H., Tao, H.Q.: Power load forecasting based on Elman neural network. In: 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, vol. Iv, pp. 374–376 (2009)Google Scholar
  6. 6.
    Cheng, Y.C., Qi, W.M., Cai, W.Y.: Dynamic properties of Elman and modified Elman neural network. In: 2002 International Conference on Machine Learning and Cybernetics, Proceedings, vols. 1–4, pp. 637–640 (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Information Engineering, Inner Mongolia University of Science and TechnologyBaotouChina
  2. 2.Haiwan Specialty Chemical Co., LtdQingdaoChina

Personalised recommendations