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Tomasz Jasiński
(Tomasz Jasinski)

Personal Details

First Name:Tomasz
Middle Name:
Last Name:Jasinski
Suffix:
RePEc Short-ID:pja639
[This author has chosen not to make the email address public]

Affiliation

Wydział Organizacji i Zarządzania
Politechnika Łódzka

Łódź, Poland
http://oizet.p.lodz.pl/oiz/
RePEc:edi:woplopl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Tomasz Jasinski & Agnieszka Scianowska, 2014. "Security Assessment and Optimization of Energy Supply," Working Papers 30/2014, Institute of Economic Research, revised Dec 2014.

Articles

  1. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  2. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
  3. Zbigniew Leszczyński & Tomasz Jasiński, 2020. "Comparison of Product Life Cycle Cost Estimating Models Based on Neural Networks and Parametric Techniques—A Case Study for Induction Motors," Sustainability, MDPI, vol. 12(20), pages 1-14, October.
  4. Tomasz Jasiński, 2020. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)," Energies, MDPI, vol. 13(5), pages 1-16, March.
  5. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
  6. Tomasz Jasinski & Agnieszka Scianowska, 2015. "Security Assessment And Optimization Of Energy Supply (Neural Networks Approach)," Oeconomia Copernicana, Institute of Economic Research, vol. 6(2), pages 129-141, June.

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

    Sorry, no citations of working papers recorded.

Articles

  1. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).

    Cited by:

    1. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    2. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    3. Kılıç Depren, Serpil & Kartal, Mustafa Tevfik & Ertuğrul, Hasan Murat & Depren, Özer, 2022. "The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods," Renewable Energy, Elsevier, vol. 186(C), pages 217-225.
    4. Zheng, Kedi & Chen, Huiyao & Wang, Yi & Chen, Qixin, 2022. "Data-driven financial transmission right scenario generation and speculation," Energy, Elsevier, vol. 238(PC).
    5. Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).

  2. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.

    Cited by:

    1. Piotr Wójcik & Krystian Andruszek, 2022. "Predicting intra‐urban well‐being from space with nonlinear machine learning," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 891-913, August.
    2. Naeher,Dominik & Narayanan,Raghavan & Ziulu,Virginia, 2021. "Impacts of Energy Efficiency Projects in Developing Countries : Evidence from a SpatialDifference-in-Differences Analysis in Malawi," Policy Research Working Paper Series 9842, The World Bank.
    3. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    4. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    5. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    6. Zhong, Liang & Liu, Xiaosheng & Ao, Jianfeng, 2022. "Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road," Energy, Elsevier, vol. 239(PA).

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (1) 2015-01-31
  2. NEP-ENE: Energy Economics (1) 2015-01-31

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