Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation

Prasad, Ramendra and Ali, Mumtaz and Kwan, Paul and Khan, Huma (2019) Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Applied Energy, 236. pp. 778-792. ISSN 0306-2619

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 13 Jun 2019 05:22
Last Modified: 24 Jun 2019 06:03
Uncontrolled Keywords: solar radiation; energy; multivariate empirical mode decomposition; ant colony optimization; random forest
Fields of Research : 05 Environmental Sciences > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.apenergy.2018.12.034
URI: http://eprints.usq.edu.au/id/eprint/35458

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