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ORIGINAL RESEARCH article

Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1447745
This article is part of the Research Topic Sustainable Electronics and Devices View all articles

Time-series forecasting of microbial fuel cell energy generation using deep learning

Provisionally accepted
Adam Hess-Dunlop Adam Hess-Dunlop 1*Harshitha Kakani Harshitha Kakani 2Stephen Taylor Stephen Taylor 2Dylan Louie Dylan Louie 2Jason Eshraghian Jason Eshraghian 2Colleen Josephson Colleen Josephson 2*
  • 1 Arizona State University, Tempe, United States
  • 2 University of California, Santa Cruz, Santa Cruz, California, United States

The final, formatted version of the article will be published soon.

    Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% Mean Average Percent Error (MAPE) for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

    Keywords: Microbial fuel cell (MFC), soil microbial fuel cells, deep learning, Energy prediction, Quantile regresision, LSTM (Long Short Term Memory Networks), Time series analsis, Intermittent computing

    Received: 12 Jun 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Hess-Dunlop, Kakani, Taylor, Louie, Eshraghian and Josephson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Adam Hess-Dunlop, Arizona State University, Tempe, United States
    Colleen Josephson, University of California, Santa Cruz, Santa Cruz, 95064, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.