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

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

doi:10.1016/B978-0-444-63428-3.50259-9

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

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

Power Systems Engineering

Research output: Contribution to conference › Paper › peer-review

4 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 language	American English
Pages	1527-1532
Number of pages	6
DOIs	https://doi.org/10.1016/B978-0-444-63428-3.50259-9 https://doi.org/10.1016/B978-0-444-63428-3.50259-9
State	Published - 2016
Event	26th European Symposium on Computer Aided Process Engineering - Portoroz, Slovenia Duration: 12 Jun 2016 → 15 Jun 2016

Conference

Conference	26th European Symposium on Computer Aided Process Engineering
City	Portoroz, Slovenia
Period	12/06/16 → 15/06/16

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

NREL Publication Number

NREL/CP-5D00-67176

Keywords

neural networks
renewable energy forecasting

Access to Document

Cite this

@conference{e2573ea711894c2aaecd240da3f08222,

title = "Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries",

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.",

keywords = "neural networks, renewable energy forecasting",

author = "Brian Hodge and Soobin Lee and Jun-Hyung Ryu and In-Beum Lee",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.; 26th European Symposium on Computer Aided Process Engineering ; Conference date: 12-06-2016 Through 15-06-2016",

year = "2016",

doi = "10.1016/B978-0-444-63428-3.50259-9",

language = "American English",

pages = "1527--1532",

}

Hodge, B, Lee, S, Ryu, J-H & Lee, I-B 2016, 'Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries', Paper presented at 26th European Symposium on Computer Aided Process Engineering, Portoroz, Slovenia, 12/06/16 - 15/06/16 pp. 1527-1532. https://doi.org/10.1016/B978-0-444-63428-3.50259-9, https://doi.org/10.1016/B978-0-444-63428-3.50259-9

TY - CONF

T1 - Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

AU - Hodge, Brian

AU - Lee, Soobin

AU - Ryu, Jun-Hyung

AU - Lee, In-Beum

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - neural networks

KW - renewable energy forecasting

UR - http://www.scopus.com/inward/record.url?scp=84994219990&partnerID=8YFLogxK

U2 - 10.1016/B978-0-444-63428-3.50259-9

DO - 10.1016/B978-0-444-63428-3.50259-9

M3 - Paper

AN - SCOPUS:84994219990

SP - 1527

EP - 1532

T2 - 26th European Symposium on Computer Aided Process Engineering

Y2 - 12 June 2016 through 15 June 2016

ER -

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

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

Access to Document

Other files and links

Fingerprint

Cite this