Katsuyuki Tanaka

Personal Details

First Name:	Katsuyuki
Middle Name:
Last Name:	Tanaka
Suffix:
RePEc Short-ID:	pta859
	[This author has chosen not to make the email address public]

Affiliation

Research Institute for Economics and Business Administration (RIEB)
Kobe University

Kobe, Japan
http://www.rieb.kobe-u.ac.jp/
RePEc:edi:rikobjp (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
   • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.

   Cited by:

   1. Hitoshi Hamori & Shigeyuki Hamori, 2020. "Does Ensemble Learning Always Lead to Better Forecasts?," Applied Economics and Finance, Redfame publishing, vol. 7(2), pages 51-56, March.
   2. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
   3. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
   4. Tai-Hock Kuek & Chin-Hong Puah & M. Affendy Arip, 2020. "Financial Vulnerability and Economic Dynamics in Malaysia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 55-73.
   5. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
   6. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
   7. Lei Xu & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform," JRFM, MDPI, vol. 11(4), pages 1-11, December.

2. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Forecasting the Vulnerability of Industrial Economic Activities: Predicting the Bankruptcy of Companies," Discussion Papers 1721, Graduate School of Economics, Kobe University.

   Cited by:

   1. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
      • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
   2. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.

Articles

1. Zhaojie Luo & Xiaojing Cai & Katsuyuki Tanaka & Tetsuya Takiguchi & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks," JRFM, MDPI, vol. 12(1), pages 1-13, January.

   Cited by:

   1. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
   2. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
   3. Shima Nabiee & Nader Bagherzadeh, 2023. "Stock Trend Prediction: A Semantic Segmentation Approach," Papers 2303.09323, arXiv.org.
   4. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
   5. Shigeyuki Hamori, 2020. "Empirical Finance," JRFM, MDPI, vol. 13(1), pages 1-3, January.
   6. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).

2. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Analyzing Industry‐Level Vulnerability By Predicting Financial Bankruptcy," Economic Inquiry, Western Economic Association International, vol. 57(4), pages 2017-2034, October.

   Cited by:

   1. Wenting Zhang & Shigeyuki Hamori, 2020. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?," Energies, MDPI, vol. 13(9), pages 1-22, May.
   2. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
   3. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
   4. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
   5. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
   6. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
   7. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.

3. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
   • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
   See citations under working paper version above.
4. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.

   Cited by:

   1. Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit spread approximation and improvement using random forest regression," Post-Print hal-03241566, HAL.
      • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit Spread Approximation and Improvement using Random Forest Regression," Post-Print hal-02057019, HAL.
      • Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
      • Mathieu Mercadier & Jean-Pierre Lardy, 2021. "Credit spread approximation and improvement using random forest regression," Papers 2106.07358, arXiv.org.
   2. Rezaei , Pooria & Ebrahimi , Seyed Babak & Azin , Pejman, 2019. "Evaluating the Application of a Financial Early Warning System in the Iranian Banking System," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 14(2), pages 177-204, April.
   3. Wenting Zhang & Shigeyuki Hamori, 2020. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?," Energies, MDPI, vol. 13(9), pages 1-22, May.
   4. Hallman, Nicholas J. & Kartapanis, Antonis & Schmidt, Jaime J., 2022. "How do auditors respond to competition? Evidence from the bidding process," Journal of Accounting and Economics, Elsevier, vol. 73(2).
   5. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
   6. Irfan Nurfalah & Aam Slamet Rusydiana & Nisful Laila & Eko Fajar Cahyono, 2018. "Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach التحذير المبكر من الأزمات المصرفية في النظام المالي المزدوج في إندونيسيا: مقاربة ماركوف للتحويل," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(2), pages 133-156, July.
   7. Mirjana Jemović & Srđan Marinković, 2021. "Determinants of financial crises—An early warning system based on panel logit regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 103-117, January.
   8. Asyrofa Rahmi & Hung-Yuan Lu & Deron Liang & Dinda Novitasari & Chih-Fong Tsai, 2023. "Role of Comprehensive Income in Predicting Bankruptcy," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 689-720, August.
   9. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
      • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
   10. Deng, Shangkun & Huang, Xiaoru & Zhu, Yingke & Su, Zhihao & Fu, Zhe & Shimada, Tatsuro, 2023. "Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
   11. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
   12. Hitoshi Hamori & Shigeyuki Hamori, 2020. "Does Ensemble Learning Always Lead to Better Forecasts?," Applied Economics and Finance, Redfame publishing, vol. 7(2), pages 51-56, March.
   13. Alessandro Bitetto & Paola Cerchiello & Charilaos Mertzanis, 2021. "A data-driven approach to measuring financial soundness throughout the world," DEM Working Papers Series 199, University of Pavia, Department of Economics and Management.
   14. Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
   15. Manthoulis, Georgios & Doumpos, Michalis & Zopounidis, Constantin & Galariotis, Emilios, 2020. "An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks," European Journal of Operational Research, Elsevier, vol. 282(2), pages 786-801.
   16. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
   17. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
   18. Maria Ludovica Drudi & Stefano Nobili, 2021. "A liquidity risk early warning indicator for Italian banks: a machine learning approach," Temi di discussione (Economic working papers) 1337, Bank of Italy, Economic Research and International Relations Area.
   19. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
   20. Jinxi Chen & Bowen Cai, 2024. "AIIB Investment and Economic Development of India: The Case of the Gujarat Road Project," JRFM, MDPI, vol. 17(2), pages 1-25, February.
   21. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
   22. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," IWH Discussion Papers 2/2019, Halle Institute for Economic Research (IWH).
       • Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
   23. Yuxi Heluo & Kexin Wang & Charles W. Robson, 2023. "Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis," Papers 2311.13046, arXiv.org.
   24. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
   25. Durand, Pierre & Le Quang, Gaëtan, 2022. "Banks to basics! Why banking regulation should focus on equity," European Journal of Operational Research, Elsevier, vol. 301(1), pages 349-372.
   26. Cristina Zeldea, 2020. "Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions," Administrative Sciences, MDPI, vol. 10(3), pages 1-14, August.
   27. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
   28. Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
   29. Alexandr Patalaha & Maria A. Shchepeleva, 2023. "Bank Crisis Management Policies and the New Instability," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 43-60, December.
   30. Ronghua Xu & Yiran Liu & Meng Liu & Chengang Ye, 2023. "Sustainability of Shipping Logistics: A Warning Model," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
   31. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
   32. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
   33. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
   34. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
   35. Kristóf, Tamás & Virág, Miklós, 2022. "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, vol. 61(C).
   36. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
   37. Buch, Claudia M. & Vogel, Edgar & Weigert, Benjamin, 2018. "Evaluating macroprudential policies," ESRB Working Paper Series 76, European Systemic Risk Board.
   38. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
   39. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
   40. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
   41. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.

5. Burer, M. & Tanaka, K. & Favrat, D. & Yamada, K., 2003. "Multi-criteria optimization of a district cogeneration plant integrating a solid oxide fuel cell–gas turbine combined cycle, heat pumps and chillers," Energy, Elsevier, vol. 28(6), pages 497-518.

   Cited by:

   1. Chitsaz, Ata & Hosseinpour, Javad & Assadi, Mohsen, 2017. "Effect of recycling on the thermodynamic and thermoeconomic performances of SOFC based on trigeneration systems; A comparative study," Energy, Elsevier, vol. 124(C), pages 613-624.
   2. Li, HongQiang & Kang, ShuShuo & Yu, Zhun & Cai, Bo & Zhang, GuoQiang, 2014. "A feasible system integrating combined heating and power system with ground-source heat pump," Energy, Elsevier, vol. 74(C), pages 240-247.
   3. Mehrpooya, Mehdi, 2016. "Conceptual design and energy analysis of novel integrated liquefied natural gas and fuel cell electrochemical power plant processes," Energy, Elsevier, vol. 111(C), pages 468-483.
   4. Li, Hongtao & Marechal, Francois & Favrat, Daniel, 2010. "Power and cogeneration technology environomic performance typification in the context of CO2 abatement part I: Power generation," Energy, Elsevier, vol. 35(8), pages 3143-3154.
   5. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 535-551, April.
   6. Gebreslassie, Berhane H. & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano & Boer, Dieter, 2010. "A systematic tool for the minimization of the life cycle impact of solar assisted absorption cooling systems," Energy, Elsevier, vol. 35(9), pages 3849-3862.
   7. Girardin, Luc & Marechal, François & Dubuis, Matthias & Calame-Darbellay, Nicole & Favrat, Daniel, 2010. "EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas," Energy, Elsevier, vol. 35(2), pages 830-840.
   8. Zabala, Laura & Febres, Jesus & Sterling, Raymond & López, Susana & Keane, Marcus, 2020. "Virtual testbed for model predictive control development in district cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
   9. Jradi, M. & Riffat, S., 2014. "Tri-generation systems: Energy policies, prime movers, cooling technologies, configurations and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 396-415.
   10. Costa, Andrea & Paris, Jean & Towers, Michael & Browne, Thomas, 2007. "Economics of trigeneration in a kraft pulp mill for enhanced energy efficiency and reduced GHG emissions," Energy, Elsevier, vol. 32(4), pages 474-481.
   11. Li, Hongtao & Burer, Meinrad & Song, Zhi-Ping & Favrat, Daniel & Marechal, Francois, 2004. "Green heating system: characteristics and illustration with multi-criteria optimization of an integrated energy system," Energy, Elsevier, vol. 29(2), pages 225-244.
   12. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2019. "Cost sensitivity of optimal sector-coupled district heating production systems," Energy, Elsevier, vol. 166(C), pages 624-636.
   13. Rong, Aiying & Lahdelma, Risto, 2005. "An efficient linear programming model and optimization algorithm for trigeneration," Applied Energy, Elsevier, vol. 82(1), pages 40-63, September.
   14. Ramadhani, F. & Hussain, M.A. & Mokhlis, H. & Hajimolana, S., 2017. "Optimization strategies for Solid Oxide Fuel Cell (SOFC) application: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 460-484.
   15. Sharaf, Omar Z. & Orhan, Mehmet F., 2014. "An overview of fuel cell technology: Fundamentals and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 810-853.
   16. Buonomano, Annamaria & Calise, Francesco & d’Accadia, Massimo Dentice & Palombo, Adolfo & Vicidomini, Maria, 2015. "Hybrid solid oxide fuel cells–gas turbine systems for combined heat and power: A review," Applied Energy, Elsevier, vol. 156(C), pages 32-85.
   17. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
   18. Pruitt, Kristopher A. & Braun, Robert J. & Newman, Alexandra M., 2013. "Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems," Applied Energy, Elsevier, vol. 102(C), pages 386-398.
   19. Emadi, Mohammad Ali & Chitgar, Nazanin & Oyewunmi, Oyeniyi A. & Markides, Christos N., 2020. "Working-fluid selection and thermoeconomic optimisation of a combined cycle cogeneration dual-loop organic Rankine cycle (ORC) system for solid oxide fuel cell (SOFC) waste-heat recovery," Applied Energy, Elsevier, vol. 261(C).
   20. Chen, Zhang & Yiliang, Xie & Hongxia, Zhang & Yujie, Gu & Xiongwen, Zhang, 2023. "Optimal design and performance assessment for a solar powered electricity, heating and hydrogen integrated energy system," Energy, Elsevier, vol. 262(PA).
   21. Eberhard Jochem, 2004. "R&D and Innovation Policy — Preconditions for Making Steps towards a 2000 WATT/CAP Society," Energy & Environment, , vol. 15(2), pages 283-296, March.
   22. Oh, Si-Doek & Lee, Ho-Jun & Jung, Jung-Yeul & Kwak, Ho-Young, 2007. "Optimal planning and economic evaluation of cogeneration system," Energy, Elsevier, vol. 32(5), pages 760-771.
   23. Li, Yu & Rezgui, Yacine & Zhu, Hanxing, 2017. "District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 281-294.
   24. Molyneaux, A. & Leyland, G. & Favrat, D., 2010. "Environomic multi-objective optimisation of a district heating network considering centralized and decentralized heat pumps," Energy, Elsevier, vol. 35(2), pages 751-758.
   25. Wei, Dajun & Chen, Alian & Sun, Bo & Zhang, Chenghui, 2016. "Multi-objective optimal operation and energy coupling analysis of combined cooling and heating system," Energy, Elsevier, vol. 98(C), pages 296-307.
   26. Al Moussawi, Houssein & Fardoun, Farouk & Louahlia, Hasna, 2017. "Selection based on differences between cogeneration and trigeneration in various prime mover technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 491-511.
   27. Li, Hongtao & Maréchal, François & Burer, Meinrad & Favrat, Daniel, 2006. "Multi-objective optimization of an advanced combined cycle power plant including CO2 separation options," Energy, Elsevier, vol. 31(15), pages 3117-3134.
   28. Schiffmann, J. & Favrat, D., 2010. "Design, experimental investigation and multi-objective optimization of a small-scale radial compressor for heat pump applications," Energy, Elsevier, vol. 35(1), pages 436-450.
   29. Favrat, D. & Marechal, F. & Epelly, O., 2008. "The challenge of introducing an exergy indicator in a local law on energy," Energy, Elsevier, vol. 33(2), pages 130-136.
   30. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
   31. Skarvelis-Kazakos, Spyros & Papadopoulos, Panagiotis & Grau Unda, Iñaki & Gorman, Terry & Belaidi, Abdelhafid & Zigan, Stefan, 2016. "Multiple energy carrier optimisation with intelligent agents," Applied Energy, Elsevier, vol. 167(C), pages 323-335.
   32. Costa, Andrea & Bakhtiari, Bahador & Schuster, Sebastian & Paris, Jean, 2009. "Integration of absorption heat pumps in a Kraft pulp process for enhanced energy efficiency," Energy, Elsevier, vol. 34(3), pages 254-260.
   33. Mavrotas, George & Florios, Kostas & Vlachou, Dimitra, 2010. "Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters," MPRA Paper 105754, University Library of Munich, Germany.
   34. Facci, Andrea L. & Cigolotti, Viviana & Jannelli, Elio & Ubertini, Stefano, 2017. "Technical and economic assessment of a SOFC-based energy system for combined cooling, heating and power," Applied Energy, Elsevier, vol. 192(C), pages 563-574.
   35. Chinese, Damiana & Meneghetti, Antonella, 2005. "Optimisation models for decision support in the development of biomass-based industrial district-heating networks in Italy," Applied Energy, Elsevier, vol. 82(3), pages 228-254, November.
   36. Pruitt, Kristopher A. & Braun, Robert J. & Newman, Alexandra M., 2013. "Establishing conditions for the economic viability of fuel cell-based, combined heat and power distributed generation systems," Applied Energy, Elsevier, vol. 111(C), pages 904-920.
   37. Geonhui Gwak & Minwoo Kim & Dohwan Kim & Muhammad Faizan & Kyeongmin Oh & Jaeseung Lee & Jaeyoo Choi & Nammin Lee & Kisung Lim & Hyunchul Ju, 2019. "Performance and Efficiency Analysis of an HT-PEMFC System with an Absorption Chiller for Tri-Generation Applications," Energies, MDPI, vol. 12(5), pages 1-21, March.
   38. Henning, Dag & Amiri, Shahnaz & Holmgren, Kristina, 2006. "Modelling and optimisation of electricity, steam and district heating production for a local Swedish utility," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1224-1247, December.
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