Metro vehicle vibration energy harvesting dataset

Citation Author(s):
Enfang
Cui
Submitted by:
Enfang Cui
Last updated:
Tue, 05/17/2022 - 22:17
DOI:
10.21227/0jcz-t470
Research Article Link:
License:
610 Views
Categories:
Keywords:
0
0 ratings - Please login to submit your rating.

Abstract 

We collect 200 hours of metro vehicle vibration energy harvesting data in total at intervals of 2 min.

Instructions: 

I. Code description

The code includes two parts: energy allocation algorithms and energy prediction algorithms. The following algorithms were implemented:

Energy allocation

  • (1) FTF: Fast Time Fair Energy Allocation algorithm of our paper.
  • (2) MAllEC: implementation of Maximum Allowed Energy Consumption algorithm from "Mallec: Fast and optimal scheduling of energy consumption for energy harvesting devices," IEEE Internet of Things Journal, vol. 5, pp. 5132–5140, Dec 2018.
  • (3) buchli: implementation of Periodic Optimal Control algorithm from "Optimal power management with guaranteed minimum energy utilization for solar energy harvesting systems", Buchli et al, DCOSS 2015
  • (4) gorlatova: implementation of Progressive Filling algorithm from "Networking low-power energy harvesting devices: Measurements and algorithms", Gorlatova et al, INFOCOM, 2011.

Attention: The implementation of algorithm (2)(3)(4) refers to the source code eh_python of V. Cionca.

Energy Prediction

  • LSTM based predictor.

Dataset

We collect 200 hours of metro vehicle vibration energy harvesting data in total at intervals of 2 min.

II. Code File structure

  • /algorithms -- Source code of algorithms.
  • /data -- The metro vibration energy harvesting dataset we used.
  • /LSTM -- LSTM energy predictor based on tensorflow.
  • /saved_models -- Well trained LSTM model.
  • /simtest -- Code for offline and online experiments
    • /offline -- Energy allocation algorithms + offline dataset
    • /online -- Energy allocation algorithms + Energy preditor

III. Code environment requirements

  • Windows 10, IDE pycharm 2016.2.2 or higher
  • python 3.6.6
  • Tensorflow 1.10.0
  • keras 2.2.4
  • numpy 1.16.2
  • pandas 0.23.4

Comments

Ok

Submitted by Chouaib Ennawaoui on Tue, 03/22/2022 - 04:58