PV output forecasting by deep Boltzmann machines with SS-PPbSO

Shota Ogawa, Hiroyuki Mori

Research output: Contribution to journalArticle


This paper proposes an efficient method for photovoltaic (PV) system output forecasting by Deep Boltzmann Machines (DBM) with Scatter Search-Predator-Prey Brain Storm Optimization (SS-PPBSO). DBM plays a key role to extract features of input variables while SS-PPBSO is a new evolutionary computation that combines PPBSO with Scatter Search. In recent years, as renewable energy, PV systems are positively introduced into power network in Japan so that power system operation becomes complicated due to the uncertainty. To overcome this challenge, it is required to forecast PV outputs that are influenced by weather conditions significantly. This paper proposes a new efficient PV output forecasting method with DBM that makes use of SS-PPBSO in learning. The effectiveness of the proposed method is demonstrated for real data of a PV system.

Original languageEnglish
Pages (from-to)86-93
Number of pages8
JournalIEEJ Transactions on Power and Energy
Issue number2
Publication statusPublished - 1 Jan 2020



  • Deep Boltzmann machine
  • Deep neural network
  • Evolutionary computation
  • Photovoltaic generation forecasting
  • Predator-prey brain storm optimization
  • Scatter search

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