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Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Author

Listed:
  • Jing Zhao

    (Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Yaoqi Duan

    (Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

  • Xiaojuan Liu

    (Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China)

Abstract

Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T( h –1) at the prediction moment.

Suggested Citation

  • Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1900-:d:159085
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    References listed on IDEAS

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    Cited by:

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    3. Zhao, Jing & Duan, Yaoqi & Liu, Xiaojuan, 2019. "Study on the policy of replacing coal-fired boilers with gas-fired boilers for central heating based on the 3E system and the TOPSIS method: A case in Tianjin, China," Energy, Elsevier, vol. 189(C).
    4. Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.
    5. Saryazdi, Seyed mohammad Ebrahimi & Etemad, Alireza & Shafaat, Ali & Bahman, Ammar M., 2024. "A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    6. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    7. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    8. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.

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