Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

Brian Hodge, Soobin Lee, Jun-Hyung Ryu, In-Beum Lee

Research output: Contribution to conferencePaperpeer-review

3 Scopus Citations

Abstract

This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

Original languageAmerican English
Pages1527-1532
Number of pages6
DOIs
StatePublished - 2016
Event26th European Symposium on Computer Aided Process Engineering - Portoroz, Slovenia
Duration: 12 Jun 201615 Jun 2016

Conference

Conference26th European Symposium on Computer Aided Process Engineering
CityPortoroz, Slovenia
Period12/06/1615/06/16

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

NREL Publication Number

  • NREL/CP-5D00-67176

Keywords

  • neural networks
  • renewable energy forecasting

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