IoT, machine learning and photogrammetry in small hydropower towards energy and digital transition: potential energy and viability analyses




IoT, smart tools, photogrammetry, machine learning, viability design, small hydropower, energy and digital transition, internet protocol


This research aims to evaluate and put into practise the design of a small hydropower plant on a stream at São Vicente, in Madeira Island, supported by internet of things (IoT). The photogrammetry technique is also used with a comprehensive digital transformation, in which new concepts, methods and models, such as machine learning (ML), and big data analytics play an important role due to the huge availability time series that have to be exploited in hydropower design studies. Nowadays, digitalization and massive data availability are imposing new ways to address many of the current challenges associated with the energy and digital transition. This research is based on a simple small hydropower design, to present an integrated methodology using new methods assigned by an internet protocol system, which includes the development of different steps and components supported by GIS, photogrammetry and the use of advanced tools, with the support of a drone survey with internet communication (IoT) that allow the generation of experimentally-based estimates in situ characterization, the volumetric flow, the hydrological data treatment, the hydraulic calculations and economic estimations for a real hydro project. Therefore, hydrological variables, hydraulic analysis and topographical survey are carried out in the IoT application platform supported by new tools and methods to optimise the size of hydraulic structures, estimate the performance and potential of the hydropower plant towards the best solution for energy and digital transition. Firstly, the data-base for the all study and posterior sizing of the case study of hydropower plant are defined and then the corresponding analyses and results are presented. Then, the cost estimation for the construction, maintenance and operation of the selected elements that compose the hydropower topology are determined, as well as the respective economic balance, considering the annual energy production. In addition, both economic and environmental return on investment is discussed. Finally, an analysis to equate the cost estimates and the respective benefits of hydropower generation using this new approach applicability is stablished, taking into account some economic indicators to determine the profitability of the project.


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Author Biographies

Helena M. Ramos, University of Lisbon

Department of Civil Engineering, Architecture and Georesources, CERIS, Instituto Superior Técnico

Óscar E. Coronado-Hernández, Universidad Tecnológica de Bolívar

Facultad de Ingeniería


Afzal, B., Umair, M., Asadullah, S.G., & Ahmed, E. (2019). Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges. Future Generation Computer Systems, 92, 718-731.

Barros, M.T.L., Tsai., F.T.C., Yang, S., Lopes, J.E.G., & Yeh, W.W.G. (2003). Optimization of large-scale hydropower system operations. J. Water Resour. Plan. Manag., 129(3), 178-188.

Bhatia, M., & Sood, S K. (2020). Quantum Computing-Inspired Network Optimization for IoT Applications. IEEE Internet of Things Journal, 7(6), 5590-5598.

Brown, E. (2016). 21 Open-Source Projects for IoT. Retrieved October 23, 2016.

Cheng, W.K., Ileladewa, A.A., & Tan, T.B. (2019). A Personalized Recommendation Framework for Social Internet of Things (SIoT). In 2019 International Conference on Green and Human Information Technology (ICGHIT), pp. 24-29.

Chiara, B., Hans, I.S., Jiehong, K., & Zhirong, Y. (2020). Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions. Procedia Computer Science, 176, 1659-1668.

Ferreira, J.H.I., Camacho, J.R., Malagoli, J.A., & Júnior, S.C.G. (2016). Assessment of the potential of small hydropower development in Brazil. Renewable and Sustainable Energy Reviews, 56, 380-387.

Filho, G.L.T., dos Santos, I.F.S., & Barros, R.M. (2017). Cost estimate of small hydroelectric power plants based on the aspect factor. Renewable and Sustainable Energy Reviews, 77, 229-238.

Filo, G. (2023). Artificial Intelligence Methods in Hydraulic System Design. Energies, 16(8), 3320.

Finardi, E.C., & da Silva, E.L. (2006). Solving the hydro unit commitment problem via dual decomposition and sequential quadratic programming. IEEE Transactions on Power Systems, 21(2), 835-844.

Gaspar, M.A., & Portela, M.M. (2002). Contribution for the characterization of water resources in Madeira Island. Model to evaluate the superficial flow. 6º Water Congress (in Portuguese), Porto.

Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38-50.

Gillis, A. (2021). What is internet of things (IoT)? IOT Agenda. Retrieved August 17, 2021.

International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), World Bank Group (WB), & World Health Organization (WHO). (2019). Tracking SDG 7: The Energy Progress Report 2019. Washington, DC. Available online: (accessed on February 15, 2021).

International Energy Agency (IEA). (2020). Global Energy Review 2020. Available online: (accessed on February 15, 2021).

Kishore, T.S., Patro, E.R., Harish, V.S.K.V., & Haghighi, A.T. (2021). A Comprehensive Study on the Recent Progress and Trends in Development of Small Hydropower Projects. Energies, 14, 2882.

Kishore, T.S., & Vidyabharati, I.L. (2020). Characterization of high head run-of-river small hydro power plants using life cycle costing methodology. Water Energy International, 63, 42-47.

Kumar, R., Singal, S.K., Dwivedi, G., & Shukla, A.K. (2020). Development of maintenance cost correlation for high head run of river small hydro power plant. International Journal of Ambient Energy, 43, 1-14.

Lundstrom, T., Baqersad, J., & Niezrecki, C. (2013). Using High-Speed Stereophotogrammetry to Collect Operating Data on a Robinson R44 Helicopter. In Special Topics in Structural Dynamics, Volume 6, Conference Proceedings of the Society for Experimental Mechanics Series (pp. 401-410). Springer.

Mitchell, T.M. (2017). Key Ideas in Machine Learning. Machine Learning, 1-11.

Montazerolghaem, A. (2021). Software-defined Internet of Multimedia Things: Energy-efficient and Load-balanced Resource Management. IEEE Internet of Things Journal, 9(3), 2432-2442.

Muñoz, A. (2014). Machine Learning and Optimization. Courant Institute of Mathematical Sciences, 1-2.

Oliveira, R.P., Almeida, A.B., Sousa, J., Pereira, M.J., Portela, M.M., Coutinho, M.A., Ferreira, R., & Lopes, S. (2011). Evaluation of debris risk in Madeira Island: consequences of flood report (in Portuguese).

Prada, S., Perestrelo, A., Sequeira, M., Nunes, A., Figueira, C., & Cruz, J.V. (2005). Disponibilidades Hídricas na ilha da Madeira. Available online:

Quaranta, E., & Revelli, R. (2015). Output power and power losses estimation for an overshot water wheel. Renewable Energy, 83, 979-987.

Ramos, H. (Ed.) (2000). Guidelines for the design of small hydropower plants (pp. 190). CEHIDRO/WREAN/DED, ISBN 972 96346 4 5, North Ireland, UK.

Santolin, A., Cavazzini, G., Pavesi, G., Ardizzon, G., & Rossetti, A. (2011). Techno-economical method for the capacity sizing of a small hydropower plant. Energy Conversion and Management, 52, 2533-2541.

Sechin, A. (2014). Digital Photogrammetric Systems: Trends and Developments. GeoInformatics, 4, 32-34.

Singh, V.K., & Singal, S.K. (2017). Operation of hydro power plants-a review. Renewable and Sustainable Energy Reviews, 69, 610-619.

Stojković, M., Kostić, S., Prohaska, S., Plavšić, J., & Tripković, V. (2017). A New Approach for Trend Assessment of Annual Streamflows: a Case Study of Hydropower Plants in Serbia. Water Resources Management, 31(4), 1089-1103.

Sužiedelytė-Visockienė, J., Bagdžiūnaitė, R., Malys, N., & Maliene, V. (2015). Close-range photogrammetry enables documentation of environment-induced deformation of architectural heritage. Environmental Engineering and Management Journal, 14(6), 1371-1381.