TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems
With the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale...
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/4/765 |
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| author | Younjeong Lee Jongpil Jeong |
| author_facet | Younjeong Lee Jongpil Jeong |
| author_sort | Younjeong Lee |
| collection | DOAJ |
| description | With the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale home power generation systems. However, small-scale power generation systems face challenges in the development of efficient prediction models because of the lack of data and variability in power generation owing to weather conditions. In this study, we propose a novel forecasting framework that combines transfer learning and dynamic time warping (DTW) to address these issues. We present a transfer learning-based prediction system design that can maintain high prediction performance even in data-poor environments. In the process of developing a prediction model suitable for the target domain by utilizing multi-source data, we propose a data similarity evaluation method using DTW, which demonstrates excellent performance with low error rates in the MSE and MAE metrics compared with conventional long short-term memory (LSTM) and Transformer models. This research not only contributes to maximizing the energy efficiency of small-scale PV power generation systems and improving energy independence but also provides a methodology that can maintain high reliability in data-poor environments. |
| format | Article |
| id | doaj-art-e6e36b21a4d14b6d98c0f86c5b16aae4 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e6e36b21a4d14b6d98c0f86c5b16aae42025-08-20T03:12:00ZengMDPI AGEnergies1996-10732025-02-0118476510.3390/en18040765TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation SystemsYounjeong Lee0Jongpil Jeong1Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of KoreaDepartment of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of KoreaWith the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale home power generation systems. However, small-scale power generation systems face challenges in the development of efficient prediction models because of the lack of data and variability in power generation owing to weather conditions. In this study, we propose a novel forecasting framework that combines transfer learning and dynamic time warping (DTW) to address these issues. We present a transfer learning-based prediction system design that can maintain high prediction performance even in data-poor environments. In the process of developing a prediction model suitable for the target domain by utilizing multi-source data, we propose a data similarity evaluation method using DTW, which demonstrates excellent performance with low error rates in the MSE and MAE metrics compared with conventional long short-term memory (LSTM) and Transformer models. This research not only contributes to maximizing the energy efficiency of small-scale PV power generation systems and improving energy independence but also provides a methodology that can maintain high reliability in data-poor environments.https://www.mdpi.com/1996-1073/18/4/765time-series forecastingtransfer learningdynamic time warpingprediction performance optimizationTSMixer |
| spellingShingle | Younjeong Lee Jongpil Jeong TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems Energies time-series forecasting transfer learning dynamic time warping prediction performance optimization TSMixer |
| title | TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems |
| title_full | TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems |
| title_fullStr | TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems |
| title_full_unstemmed | TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems |
| title_short | TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems |
| title_sort | tsmixer and transfer learning based highly reliable prediction with short term time series data in small scale solar power generation systems |
| topic | time-series forecasting transfer learning dynamic time warping prediction performance optimization TSMixer |
| url | https://www.mdpi.com/1996-1073/18/4/765 |
| work_keys_str_mv | AT younjeonglee tsmixerandtransferlearningbasedhighlyreliablepredictionwithshorttermtimeseriesdatainsmallscalesolarpowergenerationsystems AT jongpiljeong tsmixerandtransferlearningbasedhighlyreliablepredictionwithshorttermtimeseriesdatainsmallscalesolarpowergenerationsystems |