TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems

Younjeong Lee and Jongpil Jeong ()
Additional contact information
Younjeong Lee: Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea
Jongpil Jeong: Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea

Energies, 2025, vol. 18, issue 4, 1-21

Abstract: 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.

Keywords: time-series forecasting; transfer learning; dynamic time warping; prediction performance optimization; TSMixer (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/4/765/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/4/765/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:4:p:765-:d:1585652

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().