Alexis Chavez | Renewable Energy Systems | Research Excellence Award

Mr. Alexis Chavez | Renewable Energy Systems | Research Excellence Award

PhD Candidate | Universidad Mayor De San Simón | Bolivia

Ivan Alexis Chavez Flores is a Civil Engineer and Water Resources Engineer specializing in hydraulics, hydrology, and climate-resilient water systems, currently serving as an independent consultant at ELAXIS Ingeniería and a part-time academic instructor, with professional engagements across academia, consultancy, and applied engineering projects. He holds a Bachelor of Science in Civil Engineering with specialization in Hydraulics and Hydrology, graduating with highest academic distinction, and a Master of Science in Water Resources Engineering earned with cum laude recognition, complemented by advanced training in hydrological modeling, irrigation efficiency, His achievements include competitive international scholarships, institutional commendations for leadership and academic service, professional certifications, active membership in national and international engineering and water resources associations, and participation as a research collaborator within multidisciplinary scientific networks.

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Featured Publications

Ivan Alexis Chavez Flores*, Impacts of climate change on the hydropower potential of a multipurpose storage system project in Bolivian Andes. Journal of Hydrology: Regional Studies, 2025, Article 102903.

Ivan Alexis Chavez Flores, Mauricio Villazón, Diego Inturias, Pablo Pardo, Carolina Aldunate, Crítica, análisis y relleno de las series de tiempo hidrométricas de la Amazonía Boliviana: Ajuste de curvas de descarga H–Q (Tomo 1). FAO Bolivia, 2024.

Ivan Alexis Chavez Flores*, Crítica, análisis y relleno de las series de tiempo hidrométricas de la Amazonía Boliviana: Análisis, crítica y relleno de la información hidrométrica y caudales (Tomo 2). FAO Bolivia, 2024.

Santiago Núñez Mejía, Carina Villegas-Lituma, Patricio Crespo, Mario Córdova, Ronald Gualán, Johanna Ochoa, Pablo Guzmán, Daniela Ballari, Ivan Alexis Chavez Flores et al., Downscaling precipitation and temperature in the Andes: applied methods and performance—a systematic review protocol. Environmental Evidence, 2023, 12, Article 23.

Dr. Liu Bai | Renewable Energy Systems | Editorial Board Member

Dr. Liu Bai | Renewable Energy Systems | Editorial Board Member

Postdoctoral Fellow | Harbin Institute of Technology | China

Liu Bai is an Assistant Researcher and Postdoctoral Fellow at the Harbin Institute of Technology, specializing in predictability, energy forecasting, solar forecasting, and structural failure prediction, where he contributes to advancing energy meteorology and data-driven engineering analysis. He holds a Ph.D. in Structural Engineering from the Harbin Institute of Technology and a Bachelor’s degree in Civil Engineering from Northeast Agricultural University, with academic training that supports his interdisciplinary research spanning renewable energy systems and structural behavior modeling. He also serves as a youth editor for multiple scientific journals, reviews manuscripts for leading international publications in energy and computational fields, and is an active member of professional societies, including roles within the Chinese Particle Society and the Aerosol Professional Committee, demonstrating recognized expertise and service within the research community.

Reviewer (Judge) Summary

Number of Acceptance Requests: 2 (1813 & 1851)

Total Approvals by Judge/Reviewer: 1 (1813)

Not Responded by Judge/Reviewer: 1 (1851)

Not Approved by Judge/Reviewer: 0

Profile: Scopus

Featured Publications

Liu Bai*, Predictability and forecast skill of solar irradiance across large-scale regions using advanced statistical and machine-learning frameworks. Renewable & Sustainable Energy Reviews, Accepted.

Liu Bai*, Deep-learning–enhanced solar irradiance prediction and uncertainty quantification based on multi-task and physically constrained modeling approaches. Solar Energy, Accepted.

Liu Bai, Data-driven structural performance assessment and failure prediction of reinforced concrete and masonry systems under complex loading conditions. Structural Concrete, Accepted.