A Solar Time-Based Analog Ensemble Method for Regional Solar Power Forecasting

Xinmin Zhang, Yuan Li, Siyuan Lu, Hendrik F. Hamann, Bri Mathias Hodge, Brad Lehman

Research output: Contribution to journalArticlepeer-review

150 Scopus Citations


This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Furthermore, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American mesoscale forecast system, the global forecast system, and the short-range ensemble forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error has been reduced by 13.80% to 61.21% when compared with three tested baselines.

Original languageAmerican English
Article number8353805
Pages (from-to)268-279
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Issue number1
StatePublished - Jan 2019

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/JA-5D00-71656


  • analog
  • ensemble
  • modeling
  • photovoltaic systems
  • Solar power forecasting


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