Electric vehicle (EV) - Synthetic data generator(SDG): Parametric models to generate synthetic samples of EV sessions data.
This repository has (i) Trained models that reflect real world dataset, which can be used to generate a synthetic sample of EV sessions, and (ii) Code to train SDG models from new data.
[1] Lahariya, Manu and Benoit, Dries and Develder, Chris (2020). Defining a Synthetic Data Generator for Realistic Electric Vehicle Charging Sessions. Proceedings of the Eleventh ACM International Conference on Future Energy Systems, pp. 406–407.
[2] Lahariya, Manu and Benoit, Dries and Develder, Chris (2020). Synthetic data generator for electric vehicle charging sessions: Modeling and evaluation using real-world data. Energies 2020, vol. 13.
@inproceedings{10.1145/3396851.3403509,
author = {Lahariya, Manu and Benoit, Dries and Develder, Chris},
title = {Defining a Synthetic Data Generator for Realistic Electric Vehicle Charging Sessions},
year = {2020},
publisher = {Association for Computing Machinery},
doi = {10.1145/3396851.3403509},
booktitle = {Proceedings of the Eleventh ACM International Conference on Future Energy Systems},
pages = {406–407}
}
@article{lahariya2020synthetic,
title = {Synthetic data generator for electric vehicle charging sessions: Modeling and evaluation using real-world data},
journal = {Energies},
author = {Lahariya, Manu and Benoit, Dries and Develder, Chris},
volume={13},
number={16},
pages={4211},
year={2020},
doi = {10.3390/en13164211},
publisher={Multidisciplinary Digital Publishing Institute}
}
Developed at UGent, IDlab.
A sample of EV sessions data can be generated using the python script SDG_sample_generate.py. Generate_sample() can be used
to generate and save samples to /res/generated samples.
from modeling.generate_sample import generate_sample
AM,MMc,MMe = SDG[0],SDG[1],SDG[2] # SDG = synthetic data generator
generate_sample(AM=AM, MMc=MMc, MMe=MMe,
horizon_start=horizon_start,horizon_end=horizon_end)AM, MMc and MMe are the SDG models that needs to be passed as inputs.
Models fitted on a real world data will be used as default SDG models for generation.
These models are saved in modeling/default_models/saved_models.
Generated EV sessions data will be saved on the res/generated_samples folder. Please refer to SDG_sample_generate.py for further details.
optional arguments:
-h, --help Show this help message and exit
-start_date START_DATE
First date of the horizon for data generation format:
dd/mm/YYYY.
-end_date END_DATE Last date of the horizon for data generation format:
dd/mm/YYYY.
-use USE Which kind of models to use. "default" for using the
default models "latest" for using the lastest trained
models.
-model MODEL Modeling method to be used for modeling arrival times:
* AC for arrival count models;
* IAT for inter-arrival time models.
-lambdamod LAMBDAMOD Method to be used for modeling lambda:
* AC: has two options, poisson_fit/neg_bio_reg;
* IAT: has three options, mean/loess/poly.
-verbose VERBOSE 0 to print nothing; > 0 values for printing more
information. Possible values: 0, 1, 2, 3. (integer)
Default model specifications:
- SDG:
-
AM: Arrival models can be either inter-arrival time models (IAT) or arrival count (AC) models.
- IAT models: possible modeling methods for lambda are (i) mean, (ii) poly, and (iii) loess.
- AC models: possible modeling methods for lambda are (i) poisson_fit (ii) neg_bio_reg
-
MMc: Connected time mixture model.
- Mixtures = GMMs
- Optimization = EM (MLE)
-
MMe: Energy required mixture model.
- Mixtures = GMMs
- Optimization = EM (MLE)
-
Default models for all possible methods of modeling for AM are provided and can be used.
To train the models, a (real world) dataset is required. Following columns are necessary in this EV sessions data.
| Column name | Description | Data format |
|---|---|---|
| Started | Starting date and time of the EV session | datetime (dd/mm/YYY HH:MM:SS) |
| ConnectedTime | Connection time of the EV session | Hours (float) |
| TotalEnergy | Requested energy of the EV session | kWh (float) |
| ChargePoint | Charging station | Categorical (str) |
We clean the data and prepare preprocessed data using preprocess module.
The command line script SDG_preprocessing.py can be used to create the preprocessed datasets.
Running this script will generate a 'slotted data' and a 'preprocessed data' file.
- Save the raw data file in a folder
- Run
SDG_preprocessing.py(supporting module./preprocess) - Please see /res/preprocess for generated plots and pre processed data.
optional arguments:
-h, --help Show this help message and exit
-Year YEAR Year for modeling (integer)
-Slotmins SLOTMINS Minutes in each timeslot (integer)
-create_plots CREATE_PLOTS
Indicator for creating plots
-Sessions_filename SESSIONS_FILENAME
Name of the file contaning raw data. This file must be
present in the res_folder (str)
-res_folder RES_FOLDER
Location for raw data file. Default is "./res". Inside
this directory EV session files must be present.
(string)
-verbose VERBOSE 0 to print nothing; > 0 values for printing more
information. Possible values: 0, 1, 2, 3. (integer)
NOTE: Please do not forget to give the file name and file location while calling this script
NOTE: This file should be run before ./SDG_fit.py (used for training models)
NOTE: Don't forget to install the packages in requirements.txt
(pip install -r requirements.txt)
SDG_fit.py can be used to fit the SDG models.
optional arguments:
-h, --help Show this help message and exit.
-model MODEL Modeling method to be used for modeling arrival times:
* AC for arrival count model;
* IAT for inter-arrival time model.
-lambdamod LAMBDAMOD Method to be used for modeling lambda, depending on MODEL:
* AC: has two options, poisson_fit/neg_bio_reg;
* IAT: has three options, mean/loess/poly.
-verbose VERBOSE 0 to print nothing; > 0 values for printing more
information. Possible values: 0, 1, 2, 3. (integer)