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65% confidence

The paper uses deep learning to predict microbial fuel cell energy generation, achieving MAPE of 2.33-5.71% for median voltage prediction across timescales from 3 min to 1 h.

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Representative sediment MFC — matched on the paper’s reported configuration, not its exact geometry.

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What they did

System
MFC
Substrate
complex media
Inoculum
natural soil exoelectrogens (mixed culture from soil microbial communities); no specific identification or sequencing performed

What worked

Voltage3.00V
R_int6926ohm

Abstract

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 min to 1 h, 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.

Key findings

  • LSTM models were trained to predict SMFC energy generation with varying degrees of accuracy.
  • Quantile regression was used to establish bounds on the uncertainty of predictions.
  • The models showed promise for use in robust and affordable outdoor sensor networks, particularly for farmers.

Keywords

Series (stratigraphy)Microbial fuel cellArtificial intelligenceDeep learningEnergy (signal processing)Machine learning

Identifiers

DOI
10.3389/fcomp.2024.1447745
Journal
Frontiers in Computer Science
Year
2025