Weijie1996/load_timeseries

Timeseries Data Processing

This repository contains a script for loading and processing time series data using the datasets library and converting it to a pandas DataFrame for further analysis.

Dataset

The dataset used contains time series data with the following features:

• id: Identifier for the dataset, formatted as Country_Number of Household (e.g., GE_1 for Germany, household 1).
• datetime: Timestamp indicating the date and time of the observation.
• target: Energy consumption measured in kilowatt-hours (kWh).
• category: The resolution of the time series (e.g., 15 minutes, 30 minutes, 60 minutes).

Data Sources

The research uses raw data from the following open-source databases:

• Netherlands Smart Meter Data: Liander Open Data
• UK Smart Meter Data: London Datastore
• Germany Smart Meter Data: Open Power System Data
• Australian Smarter Data:Smart-Grid Smart-City Customer Trial Data

Requirements

• Python 3.6+
• datasets library
• pandas library

You can install the required libraries using pip:

python -m pip install "dask[complete]"    # Install everything

Usage

The following example demonstrates how to load the dataset and convert it to a pandas DataFrame.

import dask.dataframe as dd

# read parquet file
df = dd.read_parquet("hf://datasets/Weijie1996/load_timeseries/30m_resolution_ge/ge_30m.parquet")

# change to pandas dataframe
df = df.compute()

Output

        id            datetime    target category
0  NL_1  2013-01-01 00:00:00  0.117475      60m
1  NL_1  2013-01-01 01:00:00  0.104347      60m
2  NL_1  2013-01-01 02:00:00  0.103173      60m
3  NL_1  2013-01-01 03:00:00  0.101686      60m
4  NL_1  2013-01-01 04:00:00  0.099632      60m

Related Work

This dataset has been utilized in the following research studies:

1. Comparative Assessment of Generative Models for Transformer- and Consumer-Level Load Profiles Generation

   • GitHub Repository: Generative Models for Customer Profile Generation

2. A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction

   • GitHub Repository: Full Convolutional Profile Flow