contextual-anomaly-detector 1.0.1

Contextual matrix profile for anomaly detection in building electrical loads

Contextual Matrix Profile Calculation Tool

Matrix Profile is an algorithm capable to discover motifs and discords in time series data. It is a powerful tool that by calculating the (z-normalized) Euclidean distance between any subsequence within a time series and its nearest neighbor it is able to provide insights on potential anomalies and/or repetitive patterns. In the field of building energy management it can be employed to detect anomalies in electrical load timeseries.

This tool is a Python implementation of the Matrix Profile algorithm that employs contextual information (such as external air temperature) to identify abnormal pattens in electrical load subsequences that start in predefined sub daily time windows, as shown in the following figure.

Table of Contents

• Usage
  • Data format
  • Run locally
  • Run with Docker
• Additional Information
• Cite
• Contributors
• License

Usage

The tool comes with a CLI that helps you to execute the script with the desired commands

$ python -m src.cmp.main -h

Matrix profile

positional arguments:
  input_file     Path to file
  variable_name  Variable name
  output_file    Path to the output file

options:
  -h, --help     show this help message and exit

The arguments to pass to the script are the following:

• input_file: The input dataset via an HTTP URL. The tool should then download the dataset from that URL; since it's a pre-signed URL, the tool would not need to deal with authentication—it can just download the dataset directly.
• variable_name: The variable name to be used for the analysis (i.e., the column of the csv that contains the electrical load under analysis).
• output_file: The local path to the output HTML report. The platform would then get that HTML report and upload it to the object storage service for the user to review later.

You can run the main script through the console using either local files or download data from an external url. This repository comes with a sample dataset (data.csv) that you can use to generate a report and you can pass the local path as input_file argument as follows:

Data format

The tool requires the user to provide a csv file as input that contains electrical power timeseries for a specific building, meter or energy system (e.g., whole building electrical power timeseries). The csv is a wide table format as follows:

timestamp,column_1,temp
2019-01-01 00:00:00,116.4,-0.6
2019-01-01 00:15:00,125.6,-0.9
2019-01-01 00:30:00,119.2,-1.2

The csv must have the following columns:

• timestamp [case sensitive]: The timestamp of the observation in the format YYYY-MM-DD HH:MM:SS. This column is supposed to be in UTC timezone string format. It will be internally transformed by the tool into the index of the dataframe.
• temp [case sensitive]: Contains the external air temperature in Celsius degrees. This column is required to perform thermal sensitive analysis on the electrical load.
• column_1: Then the dataframe may have N arbitrary columns that refers to electrical load time series. The user has to specify the column name that refers to the electrical load time series in the variable_name argument.

Run locally

Create virtual environment and activate it and install dependencies:

• Makefile

  make setup
  

• Linux:

  python3 -m venv .venv
  source .venv/bin/activate
  pip install poetry
  poetry install
  

• Windows:

  python -m venv venv
  venv\Scripts\activate
  pip install poetry
  poetry install
  

Now you can run the script from the console by passing the desired arguments. In the following we pass the sample dataset data.csv as input file and the variable Total_Power as the variable name to be used for the analysis. The output file will be saved in the results folder.

$ python -m src.cmp.main src/cmp/data/data.csv Total_Power src/cmp/results/reports/report.html

2024-08-13 12:45:42,821 [INFO](src.cmp.utils) ⬇️ Downloading file from <src/cmp/data/data.csv>
2024-08-13 12:45:43,070 [INFO](src.cmp.utils) 📊 Data processed successfully

*********************
CONTEXT 1 : Subsequences of 05:45 h (m = 23) that start in [00:00,01:00) (ctx_from00_00_to01_00_m05_45)
99.997%        0.0 sec

- Cluster 1 (1.660 s)   -> 1 anomalies
- Cluster 2 (0.372 s)   -> 3 anomalies
- Cluster 3 (0.389 s)   -> 4 anomalies
- Cluster 4 (0.593 s)   -> 5 anomalies
- Cluster 5 (-)         -> no anomalies green

[...]

2024-08-13 12:46:27,187 [INFO](__main__) TOTAL 0 min 44 s
2024-08-13 12:46:32,349 [INFO](src.cmp.utils) 🎉 Report generated successfully on src/cmp/results/reports/report.html

At the end of the execution you can find the report in the path specified by the output_file argument, in this case you will find it in the results folder.

Run with Docker

Build the docker image.

• Makefile

  make docker-build
  

• Linux:

  docker build -t cmp .
  

Run the docker image with the same arguments as before

• Makefile

  make docker-run
  

• Linux:

  docker run cmp data/data.csv Total_Power results/reports/report.html
  

At the end of the execution you can find the results in the results folder inside the docker container.

Cite

You can cite this work by using the following reference or either though this Bibtex file or the following plain text citation

Chiosa, Roberto, et al. "Towards a self-tuned data analytics-based process for an automatic context-aware detection and diagnosis of anomalies in building energy consumption timeseries." Energy and Buildings 270 (2022): 112302.

Contributors

• Author Roberto Chiosa

References

• Series Distance Matrix repository (https://github.com/predict-idlab/seriesdistancematrix)
• Stumpy Package (https://stumpy.readthedocs.io/en/latest/)

License

This code is licensed under the MIT License - see the LICENSE file for details.