A Gaussian-Gaussian-Restricted-Boltzmann-Machine-based Deep Neural Network Technique for Photovoltaic System Generation Forecasting

Shota Ogawa, Hiroyuki Mori

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

This paper proposes a new Gaussian-Gaussian-Restricted-Boltzmann-Machine-based method for forecasting photovoltaic (PV) system generation forecasting. Although renewable energy such as PV system and wind power generation has been used to suppress greenhouse gases in the world, it has a drawback that weather conditions influence the generation output significantly. Thus, it is not easy to perform Economic Load Dispatch (ELD) and Unit Commitment in power systems smoothly. From a standpoint of power system operation, more accurate predication models are required to deal with predicted values of PV system generation. In this paper, an efficient Deep Neural Network (DNN) model with Gaussian Gaussian Restricted Boltzmann Machine is presented to predict one-step-ahead PV system generation output. The model is based on Restricted Boltzmann Machine as a feature extractor and MultiLayer Perceptron (MLP) as ANN. The effectiveness of the proposed method is demonstrated for real data of a PV system.

Original languageEnglish
Pages (from-to)87-92
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number4
DOIs
Publication statusPublished - 1 Jan 2019
EventIFAC Workshop on Control of Smart Grid and Renewable Energy Systems, CSGRES 2019 - Jeju, Korea, Republic of
Duration: 10 Jun 201912 Jun 2019

Keywords

  • Artificial Intelligence
  • Forecasting
  • Power systems
  • Solar energy
  • Time-series analysis

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