Spatial- Temporal- TES: Reanalysis Dataset based Short- Term Temperature Forecasting System

In this paper, we propose a Spatial-Temporal-TES system for short-term forecasting of temperature using reanalysis datasets. We use the ERA5 reanalysis dataset generated by European Center for Medium-Range Weather Forecast (ECMWF) over the 2015-2020 period and take advantage of Triple Exponential Sm...

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Bibliographic Details
Published in2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2) pp. 1763 - 1768
Main Authors Akrami, Neda, Li, Yue, Dey, Prasanjit, Dev, Soumyabrata
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.12.2023
Subjects
Energy Internet
ERA5 reanalysis
Europe
Exponential Smoothing
Fars province
Prediction algorithms
Short-term Forecasting
Smoothing methods
System integration
Temperature
Time series analysis
Weather forecasting
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Summary:In this paper, we propose a Spatial-Temporal-TES system for short-term forecasting of temperature using reanalysis datasets. We use the ERA5 reanalysis dataset generated by European Center for Medium-Range Weather Forecast (ECMWF) over the 2015-2020 period and take advantage of Triple Exponential Smoothing (TES) algorithm which is one of the most well-known algorithms in time series prediction. The evaluation is done on an hourly value basis. Our experimental results show that the forecasting based on ERA5 exhibited good performance, and ensured a promising and reliable forecasting. Additionally, we compare our proposed approach to two other commonly used forecasting techniques: persistence forecasting and average forecasting. Our results reveal that our proposed method achieves a Root Mean Square Error of 0.57K and 1.01K for a forecasting interval of 12 and 24 hours in the future, respectively. It outperforms the other benchmarking methods.
DOI:10.1109/EI259745.2023.10512739