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Publication Metadata only A comprehensive review of data analytics and storage methods in geothermal energy operations(ELSEVIER, 2025) Basem, Ali; Al-Nussairi, Ahmed Kateb Jumaah; Khidhir, Dana Mohammad; Singh, Narinderjit Singh Sawaran; Baghoolizadeh, Mohammadreza; Fazilati, Mohammad Ali; Salahshour, Soheil; Sajadi, S. Mohammad; Hasanabad, Ali Mohammadi; University of Warith Alanbiyaa; University of Manara; Knowledge University; INTI International University; Shahrekord University; Islamic Azad University; Okan University; Bahcesehir University; Ministry of Education of Azerbaijan Republic; Khazar UniversityGeothermal energy storage (GES) systems are thoroughly examined in this research, with a focus on methods like borehole thermal energy storage (BTES), underground thermal energy storage (UTES), and aquifer thermal energy storage (ATES). It highlights the importance of thermal energy storage (TES) systems in addressing global energy challenges. The feasibility of UTES for large-scale energy storage and its integration with geothermal power plants is investigated. The ATES, with the advantage of large storage capacity and low operating costs has could be employed in regions with suitable aquifers. The adaptability of BTES to different ground conditions and its small land footprint made it a spotlight for the researchers. The study emphasizes the role of TES technologies in meeting the growing demand for renewable energy, reducing the impact of climate change, and providing efficient energy solutions for heating, ventilating, and air conditioning. HVAC systems. Also, the application of geothermal power plants and TES systems in decreasing the dependence on nonrenewable energy sources and increasing energy efficiency increase investigated. The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications.Publication Metadata only A comprehensive review of a building-integrated photovoltaic system (BIPV)(Elsevier Ltd, 2024) Chen, Lin; Baghoolizadeh, Mohammadreza; Basem, Ali A.; Ali, Sadek Habib; Ruhani, Behrooz; Sultan, Abbas J.; Salahshour, Soheil; Alizadeh, As'ad; Chen, Lin, School of Architecture, Yantai University, Yantai, China; Baghoolizadeh, Mohammadreza, Department of Mechanical Engineering, Shahrekord University, Shahr-e Kord, Iran; Basem, Ali A., Faculty of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq; Ali, Sadek Habib, Department of Electrical Engineering Techniques, Al-Amarah University College, Amarah, Iraq; Ruhani, Behrooz, Solar Energy Naqsh-e Jahan Company, Isfahan, Iran; Sultan, Abbas J., Department of Chemical Engineering, University of Technology- Iraq, Baghdad, Iraq, College of Engineering and Computing, Rolla, United States; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, Department of Mathematics and Computer Science, Lebanese American University, Beirut, Lebanon; Alizadeh, As'ad, Department of Mechanical Engineering, Urmia University, Urmia, IranBeginning in the early 1990s, photovoltaic (PV) technologies were integrated with building envelopes to reduce peak electrical load and fulfill building energy demands. The PV technologies are referred to be building-integrated (BI) PV systems when they are either incorporated or mounted to the envelopes. BIPV system groupings include BIPV roofs, BIPV facades, BIPV windows, and BIPV shadings. In this study, the technology division of photovoltaic cells and the BIPV system groupings are discussed and investigated. This evaluation addresses several variables that impact the BIPV system applications' functionality and design. The tilt angle of PV shading devices, transmittance, window-to-wall ratio (WWR), and glass orientation are the parameters that have been found. Researchers will find this review paper useful in constructing the BIPV system since it offers opportunities for future study. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Comprehensive review of green roof and photovoltaic-green roof systems for different climates to examine the energy-saving and indoor thermal comfort(Elsevier Ltd, 2025) Liao, Xiayan; Ali, Ali B.M.; Sawaran Singh, Narinderjit Singh; Baghoolizadeh, Mohammadreza; Alam, Mohammad Mahtab; Orlova, Tatyana; Salahshour, Soheil; Alizadeh, As'ad; Liao, Xiayan, Department of Fine Arts and Design, Leshan Teachers College, Leshan, China; Ali, Ali B.M., Air Conditioning Engineering Department, University of Warith Al-Anbiyaa, Karbala, Iraq; Sawaran Singh, Narinderjit Singh, Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia; Baghoolizadeh, Mohammadreza, Department of Mechanical Engineering, Shahrekord University, Shahr-e Kord, Iran; Alam, Mohammad Mahtab, Department of Basic Medical Sciences, King Khalid University, Abha, Saudi Arabia; Orlova, Tatyana, Department of Physics and Teaching Methods, National Pedagogical University of Uzbekistan, Tashkent, Uzbekistan; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, Faculty of Science and Letters, Pîrî Reis Üniversitesi, Istanbul, Turkey; Alizadeh, As'ad, Department of Civil Engineering, Cihan University-Erbil, Erbil, IraqBuilt-up regions are increasingly at risk from climate change and urban heat islands (UHIs). Solar panels and green roof systems (PV/GR) can provide several advantages to support ecologically sustainable cities. Research gaps in hot climates at the building and urban sizes are highlighted in this study, which examines the advantages of GR and combined PV/GR systems as well as their optimal design parameters. An extensive analysis of published works from the Scopus database was conducted to examine how energy-saving and indoor thermal comfort (UH-ES-ITC) was accomplished in urban structures, as well as the impact of green roofs (GR) and photovoltaic/GR systems on UHI mitigation. It's been found that, especially at building scale, GR and GR/PV systems enhance notable qualities in hot, dry locations. Sadly, not much research has been done on GR/PV systems on coupling scales. Among the research gaps identified in this study are those related to the methodology, scope, climate, objectives, variables, and features of this integration in different climate zones. Researchers and urban planners might use the findings to inform future research directions and implementation. © 2025 Elsevier B.V., All rights reserved.Publication Metadata only A comprehensive review of data analytics and storage methods in geothermal energy operations(Elsevier B.V., 2025) Basem, Ali A.; Al-Nussairi, Ahmed Kateb Jumaah; Khidhir, Dana Mohammad; Sawaran Singh, Narinderjit Singh; Baghoolizadeh, Mohammadreza; Fazilati, Mohammad Ali; Salahshour, Soheil; Sajadi, S. Mohammad; Hasanabad, Ali Mohammadi; Basem, Ali A., Faculty of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq; Al-Nussairi, Ahmed Kateb Jumaah, Al-Manara College for Medical Sciences, Amarah, Iraq; Khidhir, Dana Mohammad, Department of Petroleum Engineering, Knowledge University, Erbil, Iraq; Sawaran Singh, Narinderjit Singh, Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia; Baghoolizadeh, Mohammadreza, Department of Mechanical Engineering, Shahrekord University, Shahr-e Kord, Iran; Fazilati, Mohammad Ali, Efficiency and Smartization of Energy Systems Research Center, Khomeyni Shahr, Iran; Salahshour, Soheil, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Tuzla, Turkey, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey, Research Center of Applied Mathematics, Khazar University, Baku, Azerbaijan; Sajadi, S. Mohammad, Department of Chemistry, Payame Noor University, Tehran, Iran; Hasanabad, Ali Mohammadi, Fast Computing Center, Tehran, IranGeothermal energy storage (GES) systems are thoroughly examined in this research, with a focus on methods like borehole thermal energy storage (BTES), underground thermal energy storage (UTES), and aquifer thermal energy storage (ATES). It highlights the importance of thermal energy storage (TES) systems in addressing global energy challenges. The feasibility of UTES for large-scale energy storage and its integration with geothermal power plants is investigated. The ATES, with the advantage of large storage capacity and low operating costs has could be employed in regions with suitable aquifers. The adaptability of BTES to different ground conditions and its small land footprint made it a spotlight for the researchers. The study emphasizes the role of TES technologies in meeting the growing demand for renewable energy, reducing the impact of climate change, and providing efficient energy solutions for heating, ventilating, and air conditioning. HVAC systems. Also, the application of geothermal power plants and TES systems in decreasing the dependence on nonrenewable energy sources and increasing energy efficiency increase investigated. The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications. © 2025 Elsevier B.V., All rights reserved.