Dimitrios Bargiotas

Dimitrios Bargiotas was born in Larissa in 1958. In 1981, he received his Diploma in Electrical Engineering from the University of Patras. He continued his studies at the Department of Electrical Engineering and Computer Science at the University of Tennessee, USA, where he earned his Master of Science (MSc) and Doctorate (PhD) in 1986 and 1991, respectively.

His research interests focus on the fields of:

• Electric Power Systems
• Power System Stability
• Distribution System Automation
• Renewable Energy Sources
• Smart Grids
• Sensor Technologies
• Control Systems and Industrial Automation

Dimitrios Bargiotas served as a Professor in the Department of Electrical and Computer Engineering at the University of Thessaly, which he joined in 2017. His previous academic positions were at the University of Tennessee (1991–1993) and the TEI of Chalkida and Central Greece (1999–2017).

In December 2025, he was awarded the title of Professor Emeritus of the University of Thessaly, where he continues to be active in teaching and research.

During his time in the United States he served as an Adjunct Assistant Professor, Graduate Teaching Associate, and Graduate Teaching and Research Assistant at the University of Tennessee. In these roles, he provided independent teaching for theoretical and laboratory courses in Electric Circuits, Electric Machines, Power Systems, Control Systems, Transient Stability, and Electronics. He also served as a Consultant Engineer at the Oak Ridge National Laboratory.

His research activity during this period involved participation in numerous projects funded by EPRI, DOE, Oak Ridge National Laboratory, and the Power Electronics Application Center. His work focused on:

• Developing models and techniques for stability prediction in equivalent dynamic systems.
• Algorithms and strategies for electric load control to automate transmission and distribution networks.
• Load modeling and techniques to enhance the compatibility between power systems and power electronics.

From 1994 to 1999, he worked at the Generation and Transmission System Studies Division of the Public Power Corporation (PPC/DEH) as an external consultant/researcher. His work involved investigating transient and dynamic stability and frequency regulation for the national Generation, Transmission, and Distribution network, as well as the autonomous networks of Rhodes and Crete. He also worked on the interconnection of the national grid with other networks within the framework of UCPTE.

From April 1998 and for a period of one year, he served as Director of the Regional Energy Center of Central Greece and Euboea, promoting renewable energy sources and the rational use and conservation of electricity.

1. Vontzos, G.; Laitsos, V.; Bargiotas, ; Fevgas, A.; Daskalopulu, A.; Tsoukalas, L.H. Microgrid Multivariate Load Forecasting Based on Weighted Visibility Graph: A Regional Airport Case Study. Electricity 2025, 6, 17. https://doi.org/10.3390/electricity6020017
2. Laitsos, V.; Vontzos, G.; Paraschoudis, P.; Tsampasis, E.; Bargiotas, ; Tsoukalas, L.H. The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market. Energies 2024, 17, 5797. https://doi.org/10.3390/en17225797
3. Laitsos, V.; Vontzos, G.; Bargiotas, ; Daskalopulu, A.; Tsoukalas, L.H. Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms. Energies 2024, 17, 1625. https://doi.org/10.3390/en17071625
4. Kontogiannis, D.; Bargiotas, D.; Fevgas, A.; Daskalopulu, A.; Tsoukalas, L.H. Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation. Energies 2024, 17, 2844. https://doi.org/10.3390/en17122844
5. Laitsos, V.; Vontzos, G.; Tsiovoulos, A.; Bargiotas, ; Tsoukalas, L.H. Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting. Electronics 2024, 13, 1996. https://doi.org/10.3390/electronics13101996
6. Vontzos, G.; Laitsos, V.; Charakopoulos, A.; Bargiotas, ; Karakasidis, T.E. Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method. Dynamics 2024, 4, 337-356. https://doi.org/10.3390/dynamics4020020
7. Laitsos, V.; Vontzos, G.; Bargiotas, D.; Daskalopulu, A.; Tsoukalas, L.H. Enhanced Automated Deep Learning Application for Short-Term Load Forecasting. Mathematics 2023, 11, 2912. https://doi.org/10.3390/math11132912
8. Arvanitidis, A.I.; Bargiotas, D.; Kontogiannis, D.; Fevgas, A.; Alamaniotis, M. Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques. Energies 2022, 15, 7929. https://doi.org/10.3390/en15217929
9. Kontogiannis, D. Bargiotas, A. Daskalopulu, A. I. Arvanitidis, and L. H. Tsoukalas, “Structural Ensemble Regression for Cluster-Based Aggregate Electricity Demand Forecasting,” Electricity, vol. 3, no. 4, pp. 480–504, Oct. 2022, doi: 10.3390/electricity3040025. [Online]. Available: http://dx.doi.org/10.3390/electricity3040025
10. Panapakidis, M. Katsivelakis, and D. Bargiotas, “A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting,” Symmetry, vol. 14, no. 8, p. 1733, Aug. 2022, doi: 10.3390/sym14081733. [Online]. Available: http://dx.doi.org/10.3390/sym14081733
11. Kontogiannis, D. Bargiotas, A. Daskalopulu, A. I. Arvanitidis, and L. H. Tsoukalas, “Error Compensation Enhanced Day-Ahead Electricity Price Forecasting,” Energies, vol. 15, no. 4, p. 1466, Feb. 2022, doi: 10.3390/en15041466. [Online]. Available: http://dx.doi.org/10.3390/en15041466
12. I. Arvanitidis, D. Bargiotas, A. Daskalopulu, D. Kontogiannis, I. P. Panapakidis, and L. H. Tsoukalas, “Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting,” Energies, vol. 15, no. 4, p. 1295, Feb. 2022, doi: 10.3390/en15041295. [Online]. Available: http://dx.doi.org/10.3390/en15041295
13. Mladenov, V. Chobanov, G. C. Seritan, R. F. Porumb, B.-A. Enache, V. Vita, M. Stănculescu, T. Vu Van, and Bargiotas, “A Flexibility Market Platform for Electricity System Operators Using Blockchain Technology,” Energies, vol. 15, no. 2, p. 539, Jan. 2022 [Online]. Available: http://dx.doi.org/10.3390/en15020539
14. Laitsos, V.M.; Bargiotas, D.; Daskalopulu, A.; Arvanitidis, A.I.; Tsoukalas, L.H. An Incentive-Based Implementation of Demand Side Management in Power Systems. Energies 2021, 14, 7994. https://doi.org/10.3390/en14237994
15. Arvanitidis, A.I.; Bargiotas, ; Daskalopulu, A.; Laitsos, V.M.; Tsoukalas, L.H. Enhanced Short-Term Load Forecasting Using Artificial Neural Networks. Energies 2021, 14, 7788. https://doi.org/10.3390/en14227788
16. Kontogiannis, D. Bargiotas, A. Daskalopulu, and L. H. Tsoukalas, “A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality,” Energies, vol. 14, no. 19, p. 6088, Sep. 2021 [Online]. Available: http://dx.doi.org/10.3390/en14196088
17. Katsivelakis, D. Bargiotas, A. Daskalopulu, I. P. Panapakidis, and L. Tsoukalas, “Techno-Economic Analysis of a Stand-Alone Hybrid System: Application in Donoussa Island, Greece,” Energies, vol. 14, no. 7, p. 1868, Mar. 2021 [Online]. Available: http://dx.doi.org/10.3390/en14071868
18. Kontogiannis, D. Bargiotas, and A. Daskalopulu, “Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data,” Energies, vol. 14, no. 3, p. 752, Feb. 2021 [Online]. Available: http://dx.doi.org/10.3390/en14030752
19. Kontogiannis, D. Bargiotas, and A. Daskalopulu, “Minutely Active Power Forecasting Models Using Neural Networks,” Sustainability, vol. 12, no. 8, p. 3177, Apr. 2020 [Online]. Available: http://dx.doi.org/10.3390/su12083177
20. Alamaniotis, D. Bargiotas, & L.H. Tsoukalas, “Towards Smart Energy Systems: Application of Kernel Machine Regression for Medium Term Electricity Load Forecasting,” SpringerPlus – Engineering Section, Springer, 2016. Available: https://doi.org/10.1186/s40064-016-1665-z
21. Aphrodite Ktena, Christos Manasis, Dimitrios Bargiotas, Vasilis Katsifas, Takvor Soukissian, Harilaos Kontoyiannis, “Estimation of the Energy Potential of the Euripus’ Gulf Tidal Stream Using Channel Sea-surface Slope”, International Journal of Monitoring and Surveillance Technologies Research, October-December 2015.
22. Alamaniotis, D. Bargiotas, N. G. Bourbakis and L. H. Tsoukalas, "Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids," IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2997-3005, Nov. 2015, doi: 10.1109/TSG.2015.2421900.
23. Helen C. Leligou, Christos Masouros, Eleftherios Tsampasis, Theodore Zahariadis, Dimitrios Bargiotas, Konstantinos Papadopoulos, Stamatis Voliotis, “Reprogramming wireless sensor nodes”, IJCTT-International Journal of Computer Trends and Technology, Vol.1 Issue 2, April 2011, pp.1-9.
24. Manassis, D. Bargiotas, V. Karagiannis, “Temperature distribution sensor based on magnetostrictive delay lines”, Journal of Optoelectronics and Advanced Materials, Vol. 6, No. 2, June 2004, pp.677-680.
25. Karagiannis, C. Manassis, D. Bargiotas, “Displacement sensors using magnetostrictive delay lines”, Journal of Optoelectronics and Advanced Materials, Vol. 6, No. 2, June 2004, pp. 681- 684.
26. Bargiotas, V. Karagiannis, C. Manassis, “Pressure sensing using magnetostrictive delay lines”, Journal of Optoelectronics and Advanced Materials, Vol. 6, No. 2, June 2004, pp. 685 – 688.
27. Dimitrios Bargiotas, Vassilios Karagiannis and Christos Manassis, “Magneto-Elastic Uniformity of Glass Covered Wires Used in Magnetostrictive Delay Lines”, Sensors and Actuators: A 106 (1-3), pp 80-83, 15 September 2003.
28. Vassilios Karagiannis, Christos Manassis and Dimitrios Bargiotas, “Position Sensors Based on the Delay Line Principle”, Sensors and Actuators: A 106 (1-3), pp 183-186, 15 September 2003.
29. Christos Manassis, Dimitrios Bargiotas and Vassilios Karagiannis, “Optimized Distributed Field Sensor Based on Magnetostrictive Delay Lines”, Sensors and Actuators: A 106 (1-3), pp 30-33, 15 September 2003.
30. Gao, S. Basseas, D. T. Bargiotas and L. H. Tsoukalas, "Next-generation hearing prosthetics," in IEEE Robotics & Automation Magazine, vol. 10, no. 1, pp. 21-25, March 2003, doi: 10.1109/MRA.2003.1191707.
31. E. Fieno, D. T. Bargiotas and L. H. Tsoukalas, “Optimized Anticipatory Control Applied to Electric Power Systems”, International Journal of Computing Anticipatory Systems, published by ASBL CHAOS, ISSN 1373-5411, Belgium, 2002.
32. Bargiotas and J. D. Birdwell, "Residential air conditioner dynamic model for direct load control," in IEEE Transactions on Power Delivery, vol. 3, no. 4, pp. 2119-2126, Oct. 1988, doi: 10.1109/61.194024.

PRINCIPAL SCIENTIFIC INTERESTS

• Electric Power Systems
• Power System Stability
• Distribution System Automation
• Renewable Energy Sources
• Smart Grids
• Sensor Technologies
• Control Systems and Industrial Automation