# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 6 Conclusions  
At present, solar energy has a significant role due to the current concerns about green energy around the world. Thus, to improve the quality of energy generation processes, accurate forecasting systems are desirable. Solar irradiation data can present dynamic characteristics due to weather conditions. Therefore, single models (linear or nonlinear) may not achieve the best results. In this work, a heterogeneous dynamic ensemble (HetDS) approach was used to perform solar irradiation forecasting. Static ensemble approaches employ a group of models to predict future values. However, since the characteristics of the data may change over time, a dynamic approach could improve the results. In this sense, a dynamic ensemble composed of a pool of heterogeneous models was selected for each test pattern. As stand-alone approaches, we considered the linear ARIMA from the Box and Jenkins methodology, and a pool of machine learning models: DBN, ELM, GB, MLP, RF, and SVR. The databases considered were from four major Brazilian cities. The experimental evaluation showed that the proposal can overcome the single models in almost all scenarios with a small dispersion, revealing its high approximation capability. The dynamic selection ensemble of the forecasting models increased the accuracy of the system, showing its competitiveness. Regarding future work, we highlight the possibility of using recurrent neural networks and deep learning approaches. In addition, deeper analysis of the selection of the best lags can be considered, and the use of exogenous variables, such as precipitation, pressure, and temperature, or synthetic data, aiming to attain more accurate forecasts. Conceptualization, D.S.d.O.S.J., P.S.G.d.M.N. and J.F.L.d.O. ; methodology, D.S.d.O.S.J., P.S.G.d.M.N., J.F.L.d.O. and H.V.S. ; software, D.A.P.D., D.S.d.O.S.J. and T.M.B. ; validation, A.R.L., M.H.N.M. and F.M. ; formal analysis, P.S.G.d.M.N., J.F.L.d.O. and A.C.; investigation, D.A.P.D., D.S.d.O.S.J. and T.M.B. ; resources, D.S.d.O.S.J., P.S.G.d.M.N. and J.F.L.d.O. ; data curation, P.S.G.d.M.N., J.F.L.d.O. and A.C.P. ; writing--original draft preparation, D.A.P.D., D.S.d.O.S.J., T.M.B., P.S.G.d.M.N., J.F.L.d.O., A.R.L., H.V.S. and A.C.P. ; visualization, D.A.P.D., D.S.d.O.S.J., T.M.B., P.S.G.d.M.N. and J.F.L.d.O. ; supervision, A.C.; project administration, M.H.N.M. and J.B.d.M.F; funding acquisition, M.H.N.M. and J.B.d.M.F. All authors have read and agreed to the published version of the manuscript. Figure 11: Solar irradiation forecasting obtained by the proposed model for Salvador series (first 150 test set points). Figure 12: Solar irradiation forecasting obtained by the proposed model for São Paulo series (first 150 test set points). **Funding:** This research received external funding from Companhia Hidro Eletrica do Sao Francisco(CHESF) P&D + I N\({}^{\circ}\)02/2019, Coordination for the Improvement of Higher Education Personnel (CAPES)- Financing Code 001, Brazilian National Council for Scientific and Technological Development (CNPq), processes number 40558/2018-5, 315298/2020-0, Araucaria Foundation, process number 51497, and Foundation for support of Science and Technology of the State of Pernambuco (FACEPE) process number APQ-1252-1.03/21. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All data are publicly available at the National Institute of Meteorology (INMET) [[https://portal.inmet.gov.br/](https://portal.inmet.gov.br/)]([https://portal.inmet.gov.br/](https://portal.inmet.gov.br/)), accessed on 5 March 2022. **Acknowledgments:** This work is a result of the research and development project entitled \"Technical arrangement to increase reliability and electrical safety by applying energy storage by batteries and photovoltaic systems to the auxiliary service of 230/500 kV substations\", from the Public Call--P&D + I N\({}^{\circ}\)02/2019 with financing from Companhia Hidro Eletrica do Sao Francisco(CHESF).