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Towards Deep Learning for Predicting Microbial Fuel Cell Energy Output

Adam Hess-Dunlop, Harshitha Kakani, Colleen Josephson

TL;DR

A 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.

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 minutes to 1 hour, with results ranging from 2.33% to 5.71% 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.

Towards Deep Learning for Predicting Microbial Fuel Cell Energy Output

TL;DR

A 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.

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 minutes to 1 hour, with results ranging from 2.33% to 5.71% 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.

Paper Structure

This paper contains 30 sections, 4 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Microbial Fuel Cells
  4. Quantile Regression in Energy Forecasting
  5. SMFC Modeling for Incubation Phase
  6. Intermittently Active, SMFC-Powered Devices
  7. Task Scheduling Frameworks for Intermittently Powered Systems
  8. Methodology and Design
  9. Dataset Description
  10. Data Pre-Processing and Model Features
  11. Model Building
  12. Quantile Regression
  13. Model Evaluation
  14. MAPE
  15. Total Energy % Error
  16. ...and 15 more sections

Figures (3)

  • Figure 1: Overview and Vision. This work aims to create a predictive model for scheduling the activation of SMFC powered devices, allowing them to activate intermittently and then shut down to conserve energy.
  • Figure 2: Visual diagram of soil microbial fuel cell electrogenerative process.
  • Figure 3: Estimates and prediction interval plots for various time horizons. Lower and upper bound lines refer to the 5th and 95th percentile predictions, respectively. Plots for 3 minute time horizon omitted, but performance results are available in Table \ref{['tab:model_perf']}. These figures present a subset of the data in order to provide a more detailed view of model predictions. Dataset and code used for plotting will be open-source and available on Github and Google Colab.