Affiliation:
1. School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
2. AI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
Abstract
This paper proposes an Informer-based temperature prediction model to leverage data from an automatic weather station (AWS) and a local data assimilation and prediction system (LDAPS), where the Informer as a variant of a Transformer was developed to better deal with time series data. Recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (CNN)-based models, bi-directional long short-term memory (BLSTM)-based models, and a combination of both neural networks, CNN–BLSTM. However, these models have encountered issues due to the lack of time data integration during the training phase, which also lead to the persistence of a long-term dependency problem in the LSTM models. These limitations have culminated in a performance deterioration when the prediction time length was extended. To overcome these issues, the proposed model first incorporates time-periodic information into the learning process by generating time-periodic information and inputting it into the model. Second, the proposed model replaces the LSTM with an Informer as an alternative to mitigating the long-term dependency problem. Third, a series of fusion operations between AWS and LDAPS data are executed to examine the effect of each dataset on the temperature prediction performance. The performance of the proposed temperature prediction model is evaluated via objective measures, including the root-mean-square error (RMSE) and mean absolute error (MAE) over different timeframes, ranging from 6 to 336 h. The experiments showed that the proposed model relatively reduced the average RMSE and MAE by 0.25 °C and 0.203 °C, respectively, compared with the results of the CNN–BLSTM-based model.
Funder
Ministry of Science and ICT
GIST-MIT Research Collaboration
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference40 articles.
1. Korean Meteorological Agency (2023, February 10). Weather Forecast, Available online: https://web.kma.go.kr/eng/biz/forecast_01.jsp.
2. The quiet revolution of numerical weather prediction;Bauer;Nature,2015
3. Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts;Zhou;J. Clean. Prod.,2019
4. Ghaderi, A., Sanandaji, B.M., and Ghaderi, F. (2017). Deep forecast: Deep learning-based spatio-temporal forecasting. arXiv.
5. Deep learning framework to forecast electricity demand;Bedi;Appl. Energy,2019
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