Feature-selective ensemble learning-based long-term regional PV generation forecasting

Haneul Eom, Yongju Son, Sungyun Choi

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Because of Korea's rapid expansion in photovoltaic (PV) generation, forecasting long-term PV generation is of prime importance for utilities to establish transmission and distribution planning. However, most previous studies focused on long-term PV forecasting have been based on parametric methodologies, and most machine learning-based approaches have focused on short-term forecasting. In addition, many factors can affect local PV production, but proper feature selection is needed to prevent overfitting and multicollinearity. In this study, we perform feature-selective long-term PV power generation predictions based on an ensemble model that combines machine learning methods and traditional time-series predictions. We provide a framework for performing feature selection through correlation analysis and backward elimination, along with an ensemble prediction methodology based on feature selection. Utilities gather predictions from various sources and need to consider them to make accurate forecasts. Our ensemble method can produce accurate predictions using various prediction sources. The model with applied feature selection shows higher predictive power than other models that use arbitrary features, and the proposed feature-selective ensemble model based on a convolutional neural network shows the best predictive power.

Original languageEnglish
Article number9040551
Pages (from-to)54620-54630
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020


  • Ensemble learning
  • forecasting
  • long-term forecast
  • machine learning
  • power system planning

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


Dive into the research topics of 'Feature-selective ensemble learning-based long-term regional PV generation forecasting'. Together they form a unique fingerprint.

Cite this