Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. (15th February 2019)

• Record Type: Journal Article
• Authors: Prasad, Ramendra; Ali, Mumtaz; Kwan, Paul; Khan, Huma
• Is Part Of: Applied energy. Volume 236(2019)
• Page Start: 778
• Page End: 792
• Publication Date: 2019-02-15
• Subjects: Solar radiation -- Energy -- Multivariate empirical mode decomposition -- Ant colony optimization -- Random forest; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.12.034
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Solar radiation can provide a viable alternative to renewable energy. A multi-stage machine learning model is designed to forecast solar radiation. The proposed model outperforms comparative models in Australia's energy sites. The proposed multi-stage model can be adopted as a pertinent decision-support tool. 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.