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

Jing Zhao, Yaoqi Duan and Xiaojuan Liu
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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

Energies, 2018, vol. 11, issue 7, 1-18

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.

Keywords: uncertainty analysis; load forecasting; the Monte Carlo Method (MCM); the Support Vector Machine (SVM) model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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