Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{GHIMIRE:2018:RSE,
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author = "Sujan Ghimire and Ravinesh C. Deo and
Nathan J. Downs and Nawin Raj",
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title = "Self-adaptive differential evolutionary extreme
learning machines for long-term solar radiation
prediction with remotely-sensed MODIS satellite and
Reanalysis atmospheric products in solar-rich cities",
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journal = "Remote Sensing of Environment",
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volume = "212",
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pages = "176--198",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Satellite
solar prediction model, Particle swarm optimization,
Neural network, Support vector machine, Grid search,
Giovanni, ECMWF, Extreme learning machine",
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ISSN = "0034-4257",
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DOI = "doi:10.1016/j.rse.2018.05.003",
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URL = "http://www.sciencedirect.com/science/article/pii/S0034425718302165",
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abstract = "Designing predictive models of global solar radiation
can be an effective renewable energy feasibility
studies approach to resolve future problems associated
with the supply, reliability and dynamical stability of
consumable energy demands generated by solar-powered
electrical plants. In this paper we design and present
a new approach to predict the monthly mean daily solar
radiation (GSR) by constructing an extreme learning
machine (ELM) model integrated with the Moderate
Resolution Imaging Spectroradiometer (MODIS)-based
satellite and the European Center for Medium Range
Weather Forecasting (ECMWF) Reanalysis data for solar
rich cities: Brisbane and Townsville, Australia. A
self-adaptive differential evolutionary ELM (i.e.,
SaDE-ELM) is proposed, using a swarm-based ant colony
optimization (ACO) feature selection to select the most
important predictors for GSR, and the SaDE-ELM is then
benchmarked with nine different data-driven models: a
basic ELM, genetic programming (GP), online sequential
ELM with fixed (OS-ELM) and varying (OSVARY-ELM) input
sizes, and hybridized model including the particle
swarm optimized-artificial neural network model
(PSO-ANN), genetic algorithm optimized ANN (GA-ANN),
PSO-support vector machine model (PSO-SVR), genetic
algorithm optimized-SVR model (GA-SVR) and the SVR
model optimized with grid search (GS-SVR). A
comprehensive evaluation of the SaDE-ELM model is
performed, considering key statistical metrics and
diagnostic plots of measured and forecasted GSR. The
results demonstrate excellent forecasting capability of
the SaDE-ELM model in respect to the nine benchmark
models. SaDE-ELM outperformed all comparative models
for both tested study sites with a relative mean
absolute and a root mean square error (RRMSE) of
2.6percent and 2.3percent (for Brisbane) and 0.8percent
and 0.7percent (for Townsville), respectively. Majority
of the forecasted errors are recorded in the lowest
magnitude frequency band, to demonstrate the
preciseness of the SaDE-ELM model. When tested for
daily solar radiation forecasting using the ECMWF
Reanalysis data for Brisbane study site, the
performance resulted in an RRMSE approx 10.5percent.
Alternative models evaluated with the input data
classified into El Nino, La Nina and the positive and
negative phases of the Indian Ocean Dipole moment
(considering the impacts of synoptic-scale climate
phenomenon), confirms the superiority of the SaDE-ELM
model (with RRMSEa lteqa 13percent). A seasonal
analysis of all developed models depicts SaDE-ELM as
the preferred tool over the basic ELM and the
hybridized version of ANN, SVR and GP model. In
accordance with the results obtained through MODIS
satellite and ECMWF Reanalysis data products, this
study ascertains that the proposed SaDE-ELM model
applied with ACO feature selection, integrated with
satellite-derived data is adoptable as a qualified tool
for monthly and daily GSR predictions and long-term
solar energy feasibility study especially in data
sparse and regional sites where a satellite footprint
can be identified",
- }
Genetic Programming entries for
Sujan Ghimire
Ravinesh C Deo
Nathan J Downs
Nawin Raj
Citations