Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: a new approach
Article
Article Title | Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: a new approach |
---|---|
ERA Journal ID | 4066 |
Article Category | Article |
Authors | Deo, Ravinesh C. (Author), Sahin, Mehmet (Author), Adamowski, Jan F. (Author) and Mi, Jianchun (Author) |
Journal Title | Renewable and Sustainable Energy Reviews |
Journal Citation | 104, pp. 235-261 |
Number of Pages | 27 |
Year | 2019 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 1364-0321 |
1879-0690 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.rser.2019.01.009 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1364032119300048 |
Abstract | Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555–0.896 vs. 0.411–0.858 (RF), 0.434–0.811 (M5 Tree), and 0.113–0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715–7.191% vs. 4.907–10.784% (RF), 7.111–11.169% (M5 Tree) and 4.591–18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint. |
Keywords | satellite solar model; remote sensing; extreme learning machine; spatial forecasting |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
370201. Climate change processes | |
469999. Other information and computing sciences not elsewhere classified | |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Siirt University, Turkiye | |
McGill University, Canada | |
Peking University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q509z/universally-deployable-extreme-learning-machines-integrated-with-remotely-sensed-modis-satellite-predictors-over-australia-to-forecast-global-solar-radiation-a-new-approach
218
total views10
total downloads1
views this month0
downloads this month