Joao Leal

Professor

Institutt for ingeniørvitenskap

E-post: joao.leal@uia.no
Telefon: +47 37233139

Kontor H4032 (Jon Lilletuns vei 9, 4879 Grimstad, Norway)

Forskningsgrupper

Energisystemer

Ansvarsområde

EDUCATIONAL BACKGROUND

Ph.D. in Civil Engineering, University of Beira Interior, Portugal, 2005.

M.Sc. in Hydraulics and Water Resources, Instituto Superior Técnico, University of Lisbon, Portugal, 1999.

Licentiate Degree in Civil Engineering (Specialization of Hydraulics and Water Resources), Instituto Superior Técnico, University of Lisbon, Portugal, 1996.

WORKING EXPERIENCE

Since 2014: Professor, Department of Engineering, University of Agder, Norway.

2012-2013: Researcher, Prometeo Project, Secretary of Higher Education, Science, Technology and Innovation, Ecuador.

2008-2014: Assistant Professor, Department of Civil Engineering, Faculty of Science and Technology, New University of Lisbon, Portugal.

2005-2008: Assistant Professor, Department of Civil Engineering and Architecture, University of Beira Interior, Portugal.

1999-2005: Assistant, Department of Civil Engineering and Architecture, University of Beira Interior, Portugal.

1997-1999: Trainee Assistant, Department of Civil Engineering and Architecture, University of Beira Interior, Portugal.

1995-1996: Fellowship for Initiation to Research, Integrated System for Simulating Floods and Risk Assessment (PRAXIS/3/3.2/CEG/32/94), Instituto Superior Técnico, University of Lisbon, Portugal.

ACADEMIC MANAGEMENT EXPERIENCE

2015-2020: Study Coordinator of Master Programme in Renewable Energy, Department of Engineering, University of Agder, Norway.

2009-2014: Member of the Department Council, Department of Civil Engineering, Faculty of Science and Technology, New University of Lisbon, Portugal.

2009-2011: Member of the Scientific Committee of the Integrated Master Programme in Civil Engineering, Faculty of Science and Technology, New University of Lisbon, Portugal.

2006-2007: Director of the Civil Engineering Programme, Department of Civil Engineering and Architecture, University of Beira Interior, Portugal.

2006-2007: Member of the University Pedagogic Council, University of Beira Interior, Portugal.

2005-2008: Director of the Laboratory of Hydraulics, University of Beira Interior, Portugal.

1998-2000: Member of the Faculty Pedagogic Council, Faculty of Engineering, University of Beira Interior, Portugal.

Forskning

Faglige interesser

ACADEMIC INTERESTS

Main interests lie within data analysis, conceptual, mathematical and numerical modelling of engineering problems involving water resources. Lately, he has been focused on metaheuristic optimization of engineering systems and data-driven models for water and wind forecasting.

Teaching and supervision experience at B.Sc., M.Sc. and Ph.D. level since 1997. The courses have mainly been devoted to education within civil and renewable energy engineering. Course subjects have included:

Fluid Mechanics (hydraulic design of pipe and open channel systems)
Urban Hydraulics (water supply and drainage systems)
Hydropower (energy evaluation, design of conveyance systems, choice of turbines)
Physical and Mathematical Modelling (dimensional analysis and similarity, numerical integration, differentiation and solution of ODEs and PDEs)
Data analysis, Optimization and Modelling (data mining, data-driven models, meta-heuristic approaches)
Hydrology and Water Resources (resource evaluation, semi-empirical and statistical analysis of precipitation, runoff and floods)
Fluvial Hydraulics (conceptual modelling including compound channel configurations, vegetation, sediment transport and high transient flows)

CURRENT COURSES

ENE-418, Data Analysis and Modelling Techniques in Renewable Energy, M.Sc. Renewable Energy
ENE-410, Fluid Dynamics and Hydro Power, M.Sc. Renewable Energy
ENE-240, Hydro Power, B.Sc. Renewable Energy
BYG-226, Water and Wastewater Treatment, B.Sc. Civil Engineering

Mirza Alipour, Saba; Engeland, Kolbjørn & Leal, J B (2023). Uncertainty analysis of 100-year flood maps under climate change scenarios. Journal of Hydrology. ISSN 0022-1694. 628. doi: 10.1016/j.jhydrol.2023.130502.
Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2023). Direct and indirect short-term aggregated turbine- and farm-level wind power forecasts integrating several NWP sources. Heliyon. ISSN 2405-8440. 9(11). doi: 10.1016/j.heliyon.2023.e21479. Fulltekst i vitenarkiv
Yakoub, Ghali; Mathew, Sathyajith & Leal, João Gouveia Aparício Bento (2022). Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models. Energy. ISSN 0360-5442. 263(D). doi: 10.1016/j.energy.2022.125893. Fulltekst i vitenarkiv Vis sammendrag
Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8% and 22%, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm.
Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2022). Power production forecast for distributed wind energy systems using support vector regression. Energy Science & Engineering. ISSN 2050-0505. s. 1–12. doi: 10.1002/ese3.1295. Fulltekst i vitenarkiv Vis sammendrag
Due to the inherent intermittency in wind power production, reliable short-term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site-specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day-ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine.
Mirza Alipour, Saba; Engeland, Kolbjørn & Leal, J B (2021). Representation of 100-year design rainfall uncertainty in catchment-scale flood modeling: A MCMC Bayesian approach . Vann. ISSN 0042-2592. Fulltekst i vitenarkiv
Mirza Alipour, Saba & Leal, J B (2021). Emulation of 2d hydrodynamic flood simulations at catchment scale using ann and svr. Water. ISSN 2073-4441. 13(20), s. 1–19. doi: 10.3390/w13202858. Fulltekst i vitenarkiv
Mirza Alipour, Saba; Engeland, Kolbjørn & Leal, J B (2021). A practical methodology to perform global sensitivity analysis for 2D hydrodynamic computationally intensive simulations . Hydrology Research. ISSN 1998-9563. 52(6), s. 1309–1327. doi: 10.2166/NH.2021.243. Fulltekst i vitenarkiv
Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2020). Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1618. doi: 10.1088/1742-6596/1618/6/062034. Fulltekst i vitenarkiv
Mirza Alipour, Saba & Leal, J B (2019). Return levels uncertainty under effect of climate change. Proceedings of the IAHR World Congress. ISSN 2521-7119. s. 256–264. doi: 10.3850/38WC092019-1230.
Yakoub, Ghali; Leal, J B & Tunheim, Ketil (2019). A flood forecasting model for unregulated catchments based on Artificial Neural Networks: the October 2017 flood in Tovdal’s catchment. Vann. ISSN 0042-2592. s. 103–113.
Fernandes, João Nuno; Leal, J B & Cardoso, António H. (2018). Influence of floodplain and riparian vegetation in the conveyance and structure of turbulent flow in compound channels. E3S Web of Conferences. ISSN 2267-1242. 40. doi: 10.1051/e3sconf/20184006035.
Proust, S; Fernandes, J N; Leal, J B; Riviere, N & Peltier, Y (2017). Mixing layer and coherent structures in compound channel flows: Effects of transverse flow, velocity ratio, and vertical confinement. Water Resources Research. ISSN 0043-1397. 53(4), s. 3387–3406. doi: 10.1002/2016WR019873.
Azevedo, Ricardo; Roja-Solorzano, Luis R & Leal, J B (2017). Turbulent structures, integral length scale and turbulent kinetic energy (TKE) dissipation rate in compound channel flow. Flow Measurement and Instrumentation. ISSN 0955-5986. 57, s. 10–19. doi: 10.1016/j.flowmeasinst.2017.08.009.
Proust, S; Berni, Celine; Boudou, Martin; Chiaverini, Antoine; Dupuis, Victor & Faure, Jean-Baptiste [Vis alle 28 forfattere av denne artikkelen] (2016). Predicting the flow in the floodplains with evolving land occupations during extreme flood events (FlowRes ANR project). E3S Web of Conferences. ISSN 2267-1242. 7. doi: 10.1051/e3sconf/20160704004.
Bousmar, Didier; Mathurin, B; Fernandes, J N; Filonovich, M S; Hazlewood, C & Huthoff, F [Vis alle 9 forfattere av denne artikkelen] (2016). Uniform flow in prismatic compound channel: Benchmarking numerical models. I Constantinescu, George; Garcia, Marcelo & Hanes, Dan (Red.), River Flow 2016. CRC Press. ISSN 978-1-138-02913-2. s. 272–280. doi: 10.1201/9781315644479-46.
Fernandes, J N & Leal, J B (2016). Discussion of “Improved Shiono and Knight Method for Overflow Modeling” by Hossien Kordi, Ramin Amini, Abdolreza Zahiri, and Esmaeil Kordi". Journal of hydrologic engineering. ISSN 1084-0699. 21(10). doi: 10.1061/(ASCE)HE.1943-5584.0001434.
Brito, Moises; Fernandes, J N & Leal, J B (2016). Porous media approach for RANS simulation of compound open-channel flows with submerged vegetated floodplains. Environmental Fluid Mechanics. ISSN 1567-7419. 16(6), s. 1247–1266. doi: 10.1007/s10652-016-9481-0.
Fernandes, João Nuno; Leal, J B & Cardoso, António H. (2015). Assessment of stage-discharge predictors for compound open-channels. Flow Measurement and Instrumentation. ISSN 0955-5986. 45, s. 62–67. doi: 10.1016/j.flowmeasinst.2015.04.010.
Filonovich, M S; Leal, J B & Rojas-Solórzano, L (2014). Prediction of compound channel secondary flows using anisotropic turbulence models. I Schleiss, Anton J.; De Cesare, Giovanni; Franca, Mario J. & Pfister, Michael (Red.), Proceedings of the River Flow 2014 International Conference on Fluvial Hydraulics. CRC Press. ISSN 978-1-138-02674-2. s. 163–170.
Fernandes, J N; Leal, J B & Cardoso, A H (2014). Longitudinal development of compound channel flows. I Almeida, A (Red.), 3rd IAHR Europe Congress April 14 – 16, 2014 Department of Civil Engineering, Faculty of Engineering, University of Porto, Portugal. IAHR. ISSN 978-989-96479-2-3. s. 36–46.
Fernandes, J N; Leal, J B & Cardoso, A H (2014). Improvement of the Lateral Distribution Method based on the mixing layer theory. Advances in Water Resources. ISSN 0309-1708. 69, s. 159–167. doi: 10.1016/j.advwatres.2014.04.003. Vis sammendrag
The accurate prediction of depth-averaged streamwise velocity, boundary shear stress and lateral shear stress are important requisites for the estimation of the flow depth associated with flood events in compound river channels composed of main channel and floodplain. This engineering problem may be tackled through the analytical solution of the depth-averaged momentum equation. Under uniform flow, this solution relies on the calibration of three descriptors of the bottom friction, secondary currents and lateral shear stress. In this paper, the analytical solution materialized in the Lateral Distribution Method is revisited through the consideration of a new panel division. Accurate measurements of streamwise and spanwise velocities as well as of boundary shear stress are used to obtain new predictors of the coefficients describing the effects of bottom friction, secondary currents and lateral shear. The new lateral division of the compound channel into four panels is physically based on the mixing layer width, which is computed by an iterative procedure easily implemented in practical applications.
Proust, S; Fernandes, J N; Peltier, Y; Leal, J B; Riviere, N & Cardoso, A H (2013). Turbulent non-uniform flows in straight compound open-channels. Journal of Hydraulic Research. ISSN 0022-1686. 51(6), s. 656–667. doi: 10.1080/00221686.2013.818586.
Filonovich, M S; Azevedo, R; Rojas-Solórzano, L & Leal, J B (2013). Credibility Analysis of Computational Fluid Dynamic Simulations for Compound Channel Flow. Journal of Hydroinformatics. ISSN 1464-7141. 15(3), s. 926–938. doi: 10.2166/hydro.2013.187.
Fernandes, J N; Leal, J B & Cardoso, A H (2012). Flow Structure in a Compound Channel with Smooth and Rough Floodplains. European Water. ISSN 1105-7580. 38, s. 3–12.
Ferreira, R M L; Franca, Mario J.; Leal, J B & Cardoso, A H (2012). Flow over rough mobile beds: friction factor and vertical distribution of the longitudinal mean velocity. Water Resources Research. ISSN 0043-1397. 48. doi: 10.1029/2011WR011126.
Fernandes, J N; Leal, J B & Cardoso, A H (2011). Discussion of “Apparent friction coefficient in straight compound channels”. Journal of Hydraulic Research. ISSN 0022-1686. 49(6), s. 836–839. doi: 10.1080/00221686.2011.618058.
Leal, J B; Ferreira, R M L & Cardoso, A H (2010). Geomorphic dam-break flows. Part II: numerical simulation. Proceedings of the Institution of Civil Engineers : Water Management. ISSN 1741-7589. 163(6), s. 305–313. doi: 10.1680/wama.2010.163.6.305.
Leal, J B; Ferreira, R M L & Cardoso, A H (2010). Geomorphic dam-break flows. Part I: 1D conceptual model. Proceedings of the Institution of Civil Engineers : Water Management. ISSN 1741-7589. 163(6), s. 297–304. doi: 10.1680/wama.2010.163.6.297.
Leal, J B; Ferreira, R M L & Cardoso, A H (2009). Maximum Level and Time to Peak of Dam-break Waves on Mobile Horizontal Bed. Journal of Hydraulic Engineering. ISSN 0733-9429. 135(11). doi: 10.1061/(ASCE)HY.1943-7900.0000099.
Ferreira, R M L; Franca, Mario J.; Leal, J B & Cardoso, A H (2009). Mathematical modelling of shallow flows: Closure models drawn from grain-scale mechanics of sediment transport and flow hydrodynamics. Canadian journal of civil engineering (Print). ISSN 0315-1468. 36(10), s. 1605–1621.
Leal, J B; Ferreira, R M L & Cardoso, A H (2008). Closure to “Dam-break wave-front celerity”. Journal of Hydraulic Engineering. ISSN 0733-9429. 134(6), s. 867–869. doi: 10.1061/(ASCE)0733-9429(2008)134:6(867).
Leal, J B; Ferreira, R M L & Cardoso, A H (2006). Dam-break wave-front celerity. Journal of Hydraulic Engineering. ISSN 0733-9429. 132(1), s. 69–76. doi: 10.1061/(ASCE)0733-9429(2006)132:1(69).
Ferreira, R M L; Leal, J B & Cardoso, A H (2003). Discussion of “Coupled and decoupled numerical modeling of flow and morphological evolution in alluvial rivers”. Journal of Hydraulic Engineering. ISSN 0733-9429. 129(9), s. 741–742. doi: 10.1061/(ASCE)0733-9429(2003)129:9(741).
Leal, J B; Ferreira, R M L; Franco, A B & Cardoso, A H (2002). Experimental study on dam-break flood waves over movable bed channels. International Journal of Sediment Research. ISSN 1001-6279. 17(3), s. 186–196.
Leal, J B; Ferreira, R M L; Franco, A B & Cardoso, A H (2001). Dam-break waves over movable beds. Experimental study. Recursos Hídricos. ISSN 0870-1741. 22(1), s. 25–36.
Ferreira, R M L; Leal, J B & Cardoso, A H (1999). Mathematical modelling of dam-break waves propagation over movable beds. Recursos Hídricos. ISSN 0870-1741. 20(1).

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Ferreira, R M L; Alves, E; Leal, J B & Cardoso, A H (2006). River Flow 2006. CRC Press. ISBN 0415408156. 2304 s.

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Mirza Alipour, Saba; Leal, J B & England, Kolbjørn (2022). Uncertainty Assessment of Flood Maps: A Comparison of Bootstrap and Monte Carlo Methods.
Brito, Moises; Canelas, O B; Leal, J B & Cardoso, A H (2014). 3D numerical simulation of flow at a 70º open-channel confluence.
Leal, J B (2014). Dams: advantages and disavantages. Pedra & Cal. ISSN 1645-4863. 56, s. 9–11.
Ferreira, R M L & Leal, J B (1998). 1-D Mathematical modelling of the instantaneous-dam-break flood wave over mobile bed.
Lie, Camilla; Imenes, Anne Gerd; Leal, J B & Sathyajith, Mathew (2019). Power Prediction of Photovoltaic System using Neural Network Models. Universitetet i Agder. Vis sammendrag
This work considers a photovoltaic (PV) system installed on the rooftop of Agder Energi’s headquarters located at Kjøita, Kristiansand. The system includes three different types of solar PV modules; Suntech (multi-Si), Sharp (a-Si/μ-Si) and REC (multi-Si), that have a total installed DC capacity of 45 kWp. The system is grid-connected and instrumented for research and monitoring purposes. Artificial Neural Network (ANN) models were trained to obtain the lowest mean square error (MSE), by testing different configurations using a model-based trial and error approach. The model configurations that gave the lowest (MSE) were used to predict the power production from each of the PV modules using forecasted weather parameters obtained from MEPS (MetCoOp Ensemble Prediction System), with a one-day ahead and two-days ahead forecast horizon. The input selection of the models was based on both model-free and model-based approach, where the final input selection resulted in global horizontal irradiance, wind speed, air temperature and air mass, with the power (AC) production as output (target). The results indicated that the model configurations of 20 hidden neurons in first hidden layer, and 2 hidden neurons in second hidden layer gave the lowest MSE for all PV modules. Results from the test sets showed that the best model for Suntech gave MSE = 0.0454, Sharp gave MSE = 0.0325 and REC gave MSE = 0.0492. R2-values between 0.95 and 0.96 were obtained for all three models, indicating good fitting of the predicted values and the targets. Testing the Suntech and REC models with a hold-out set provided slightly less precise predictions compared to the results from the test set, while a higher precision was found for Sharp modules. Testing the model configurations with forecasted weather parameters indicated that the forecast accuracy of the weather will influence the power prediction, and the performance parameters will be accordingly. The one-day ahead forecasts provided MSE equal to 0.2647, 0.2378 and 0.2647, and for the two-days ahead forecast horizon an MSE equal to 0.2996, 0.2252 and 0.2719 for Suntech, Sharp and REC, respectively. An error much higher compared to the test set and hold-out set for the models, which inevitably was expected due to the weather forecast uncertainties. Based on the findings in this work, it can be concluded that a further optimization of the models will be necessary before obtaining even more precise predictions. However, the models did show good fitting for several days and a potential for using ANN models for power prediction of PV modules.
Leal, J B & Filonovich, M S (2015). Numerical modelling of compound channel flow. Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa.
Leal, J B (2005). Experimental and mathematical modelling of dam-break waves propagation over movable beds. University of 'Beira Interior'.
Leal, J B (1999). Mathematical modelling of dam-break waves propagation over movable beds. Instituto Superior Técnico.

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Publisert 16. apr. 2024 10:57 - Sist endret 16. apr. 2024 10:57