Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression
File(s)
Accepted version
Author(s)
Sharifzadeh, Mandi
Sikinioti-Lock, Alexandra
Shah, Nilay
Type
Journal Article
Abstract
Renewable energy from wind and solar resources can contribute significantly to the decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless integration with the grid poses significant challenges due to their intermittent generation patterns, which is intensified by the existing uncertainties and fluctuations from the demand side. A resolution is increasing energy storage and standby power generation which results in economic losses. Alternatively, enhancing the predictability of wind and solar energy as well as demand enables replacing such expensive hardware with advanced control and optimization systems. The present research contribution establishes consistent sets of data and develops data-driven models through machine-learning techniques. The aim is to quantify the uncertainties in the electricity grid and examine the predictability of their behaviour. The predictive methods that were selected included conventional artificial neural networks (ANN), support vector regression (SVR) and Gaussian process regression (GPR). For each method, a sensitivity analysis was conducted with the aim of tuning its parameters as optimally as possible. The next step was to train and validate each method with various datasets (wind, solar, demand). Finally, a predictability analysis was performed in order to ascertain how the models would respond when the prediction time horizon increases. All models were found capable of predicting wind and solar power, but only the neural networks were successful for the electricity demand. Considering the dynamics of the electricity grid, it was observed that the prediction process for renewable wind and solar power generation, and electricity demand was fast and accurate enough to effectively replace the alternative electricity storage and standby capacity.
Date Issued
2019-07-01
Date Acceptance
2019-03-19
Citation
Renewable and Sustainable Energy Reviews, 2019, 108, pp.513-538
ISSN
1364-0321
Publisher
Elsevier
Start Page
513
End Page
538
Journal / Book Title
Renewable and Sustainable Energy Reviews
Volume
108
Copyright Statement
© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Engineering & Physical Science Research Council (E
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000465195700034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/P024807/1
Subjects
Science & Technology
Technology
Green & Sustainable Science & Technology
Energy & Fuels
Science & Technology - Other Topics
Machine-learning
Big data
Renewable wind and solar power
Electricity demand
Artificial neural networks (ANN)
Support vector regression (SVR)
Gaussian process regression (GPR)
TERM WIND-SPEED
VARIATIONAL MODE DECOMPOSITION
WAVELET PACKET DECOMPOSITION
ELECTRICITY PRICE
SOLAR-RADIATION
HYBRID MODEL
TIME-SERIES
SMART GRIDS
FORECASTING TECHNIQUES
LSTM NETWORK
Publication Status
Published
Date Publish Online
2019-04-10