IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1581-d1058072.html
   My bibliography  Save this article

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

Author

Listed:
  • Roberto Morcillo-Jimenez

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • Karel Gutiérrez-Batista

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • Juan Gómez-Romero

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

Suggested Citation

  • Roberto Morcillo-Jimenez & Karel Gutiérrez-Batista & Juan Gómez-Romero, 2023. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data," Energies, MDPI, vol. 16(4), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1581-:d:1058072
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1581/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1581/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Deyslen Mariano-Hernández & Luis Hernández-Callejo & Martín Solís & Angel Zorita-Lamadrid & Oscar Duque-Pérez & Luis Gonzalez-Morales & Felix Santos García & Alvaro Jaramillo-Duque & Adalberto Ospino-, 2022. "Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    3. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    4. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Zhou, Jing & Zhang, Li & Fan, Lin & Yang, Zhaoming & Xie, Fei & Zuo, Lili & Zhang, Jinjun, 2023. "A systematic framework for the assessment of the reliability of energy supply in Integrated Energy Systems based on a quasi-steady-state model," Energy, Elsevier, vol. 263(PB).
    5. Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.
    6. Quoilin, Sylvain & Kavvadias, Konstantinos & Mercier, Arnaud & Pappone, Irene & Zucker, Andreas, 2016. "Quantifying self-consumption linked to solar home battery systems: Statistical analysis and economic assessment," Applied Energy, Elsevier, vol. 182(C), pages 58-67.
    7. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    3. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    4. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    5. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    6. Nafeesa Javed & Muhammad Javaid Iqbal & Sohail Masood & Laiba Rehman & Saba Ramzan, 2024. "The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 777-783.
    7. Klamka, Jonas & Wolf, André & Ehrlich, Lars G., 2020. "Photovoltaic self-consumption after the support period: Will it pay off in a cross-sector perspective?," Renewable Energy, Elsevier, vol. 147(P1), pages 2374-2386.
    8. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    9. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    10. Villa-Arrieta, Manuel & Sumper, Andreas, 2019. "Economic evaluation of Nearly Zero Energy Cities," Applied Energy, Elsevier, vol. 237(C), pages 404-416.
    11. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & José Matas, 2021. "Individual vs. Community: Economic Assessment of Energy Management Systems under Different Regulatory Frameworks," Energies, MDPI, vol. 14(3), pages 1-27, January.
    13. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    14. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Jeoung, Jaewon & Hong, Taehoon, 2024. "Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    16. Wang, Yi & Sun, Qi & Zhang, Zilu & Chen, Liqing, 2022. "A risk measure of the stock market that is based on multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    17. Freitas Gomes, Icaro Silvestre & Perez, Yannick & Suomalainen, Emilia, 2020. "Coupling small batteries and PV generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    18. Paolo Corti & Luisa Capannolo & Pierluigi Bonomo & Pierluigi De Berardinis & Francesco Frontini, 2020. "Comparative Analysis of BIPV Solutions to Define Energy and Cost-Effectiveness in a Case Study," Energies, MDPI, vol. 13(15), pages 1-23, July.
    19. Hirschburger, Rafael & Weidlich, Anke, 2020. "Profitability of photovoltaic and battery systems on municipal buildings," Renewable Energy, Elsevier, vol. 153(C), pages 1163-1173.
    20. Lozinskaia, Agata & Redkina, Anastasiia & Shenkman, Evgeniia, 2020. "Electricity consumption forecasting for integrated power system with seasonal patterns," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 60, pages 5-25.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1581-:d:1058072. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.