PV output forecasting by deep Boltzmann machines with SS‐PPBSO

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.

• Publication year

  2020

• Venue

  Electrical engineering in Japan (Print)

• Publication date

  2020-02-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1002/eej.23274
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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