Bayesian probabilistic photovoltaic power forecasting and stochastic model-predictive control for an agricultural microgrid

Brown P., GÖL M.

Solar Energy, cilt.304, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 304
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.solener.2025.114197
  • Dergi Adı: Solar Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Environment Index, Geobase, INSPEC
  • Anahtar Kelimeler: Bayesian inference, Energy storage, Microgrid, Optimization, Photovoltaic systems, Probabilistic forecasting
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

This paper describes a novel probabilistic forecasting method for photovoltaic power for use in energy management for an agricultural microgrid. The forecasting method utilizes recent historical data and a general weather forecast to fit a spline + Gaussian process (GP) model using no-u-turn sampling (NUTS) to infer model parameters in a Bayesian modeling framework. The method seamlessly transitions from a near-term forecast dominated by recent output to a regime where output is dominated by the meteorological forecast. The forecasts are evaluated using proper scoring rules for multivariate probabilistic forecasts and are compared to a reference multivariate persistence forecast and to an LSTM-based forecasting method. The probabilistic method is integrated into a simulation of stochastic model-predictive control (SMPC) for an off-grid agricultural microgrid incorporating photovoltaic generation, a battery storage system, irrigation pumping, and local electrical loads. A 20 %–35 % reduction in simulated operating cost is achieved using the probabilistic forecast compared to a simple expected-value point forecast.