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

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.
Authors:
; ; ;
Publication Date:
OSTI Identifier:
1326726
Report Number(s):
NREL/CP-5D00-67176
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 26th European Symposium on Computer Aided Process Engineering, 12-15 June 2016, Portoroz, Slovenia
Publisher:
Cambridge, MA: Elsevier
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY renewable energy forecasting; neural networks