Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation
Article
Article Title | Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation |
---|---|
ERA Journal ID | 4005 |
Article Category | Article |
Authors | Prasad, Ramendra (Author), Ali, Mumtaz (Author), Kwan, Paul (Author) and Khan, Huma (Author) |
Journal Title | Applied Energy |
Journal Citation | 236, pp. 778-792 |
Number of Pages | 15 |
Year | 2019 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0306-2619 |
1872-9118 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.apenergy.2018.12.034 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S030626191831849X |
Abstract | Solar energy is an alternative renewable energy resource that has the potential of cleanly addressing the increasing demand for electricity in the modern era to overcome future energy crises. In this paper, a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest (i.e., MEMD-ACO-RF) is designed to forecast monthly solar radiation (Rn). In the first stage, the proposed multi-stage MEMD-ACO-RF model, the MEMD algorithm demarcates the multivariate climate data from January 1905 to June 2018 into resolved signals i.e., intrinsic mode functions (IMFs) and a residual component. After computing the multivariate IMFs, the ant colony optimization (ACO) algorithm is used to determine the best IMFs based features for model development by incorporating the historical lagged data at (t − 1) in the second stage. The RF model at the third stage is applied to the selected IMFs to forecast monthly Rn. The results are benchmarked with M5 tree (M5tree) and minimax probability machine regression (MPMR) models integrated with MEMD and ACO, to develop the comparative hybrid MEMD-ACO-M5tree and MEMD-ACO-MPMR models respectively. The multi-stage MEMD-ACO-RF model is also evaluated against the standalone RF, M5tree and MPMR models. The proposed multi-stage MEMD-ACO-RF with comparative models is tested geographically in three locations of the Queensland state, in Australia. Based on robust evaluation metrics, the proposed multi-stage MEMD-ACO-RF model outperformed models that were compared during the testing phase and has shown the prospects of an accurate forecasting tool. The proposed multi-stage MEMD-ACO-RF model can be considered as a pertinent decision-support framework for monthly Rn forecasting. |
Keywords | solar radiation; energy; multivariate empirical mode decomposition; ant colony optimization; random forest |
ANZSRC Field of Research 2020 | 419999. Other environmental sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
University of New England | |
University of Management and Technology, Pakistan | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5043/designing-a-multi-stage-multivariate-empirical-mode-decomposition-coupled-with-ant-colony-optimization-and-random-forest-model-to-forecast-monthly-solar-radiation
194
total views11
total downloads0
views this month0
downloads this month