poernahi / Building_energy_management_toolkit

A framework for a data-driven, multi-data sourced building energy management toolkit as a synthesis of established data analysis approaches in the literature.

Building Energy Management Toolkit

Updated May 27, 2021 - This repository contains a preliminary version of a multi-sourced, data-driven toolkit for addressing building energy deficiencies and is open-source for others to learn from, adapt, and foster into more specialised versions of multi-sourced, data-driven toolkits. At this stage, the toolkit is undergoing active development. Additions and revisions are expected and the repository will be updated periodically to reflect the most recent changes. Sample data are included (found in sampleData folder) and sample visuals and key performance indicators (KPIs) from the sample data are found in the "outputs" subfolder in the "toolkit" folder.

Downloading the toolkit

Download the contents of the repository. Ensure the folder titled "toolkit" contains nine .py files and the subfolders titled "outputs" and "reports" with its subfolders are included.

metadata.py energyBaseline.py ahuAnomaly.py zoneAnomaly.py endUseDisaggregation.py occupancy.py complaintsAnalytics.py generate_report.py exe.py outputs reports

Getting started

1. Download and install Anaconda:

https://www.anaconda.com/products/individual

2. Set the path to the location where Anaconda was installed.

Press the windows key --> type environment --> select Environmental Variables --> Select the path variable and select Edit --> Add the directories where Anaconda is installed. These should be C:\Users*Username*\anaconda3 and C:\Users*Username*\anaconda3\Library\bin

3. Create a virtual environment and install the required packages. In command prompt, create a new virtual environment and call it "newEnv" with the following command line:

conda create -n newEnv python=3.8 anaconda

4. Activate the new virtual environment:

conda activate newEnv

5. Install the following required packages using pip:

pip install geneticalgorithm

pip install editdistance

pip install ruptures

pip install python-docx

6. Navigate to the directory titled "toolkit":

cd C:\Users...\toolkit

7. Run the exe.py file:

python exe.py

Using the toolkit

To be updated...

Sample data, visuals, and reports

Sample data are provided in the folder titled "sampleData" and sample visualizations from sample data are included in the subfolder titled "outputs" within the "toolkit" folder. Sample reports are also included in the subfolder titled "reports."

Editing the toolkit

The authors encourage community-driven efforts to improve and modify the toolkit. Refining the functions should not be limited to improving reliability but also to improve robustness. Derivations of multi-sourced toolkits incorporating additional, reduced, or altered functions to suit a particular set of buildings, or even establish explicit interdependencies between functions, are also encouraged. The toolkit is intended to act as a framework to initiate such efforts.

Reference documentation

Framework of the toolkit

This section will be updated when the reference documentation is available

Metadata inferencing function (metadata.py)

Chen et al., "A Metadata Inference Method for Building Automation Systems With Limited Semantic Information," 2020. https://doi.org/10.1109/TASE.2020.2990566

Baseline energy function (energyBaseline.py)

Gunay et al., "Detection and interpretation of anomalies in building energy use through inverse modeling," 2019. https://doi.org/10.1080/23744731.2019.1565550

Afroz et al., "An inquiry into the capabilities of baseline building energy modelling approaches to estimate energy savings," 2021. https://doi.org/10.1016/j.enbuild.2021.111054

AHU anomaly detection function (ahuAnomaly.py)

Gunay and Shi, "Cluster analysis-based anomaly detection in building automation systems," 2020. https://doi.org/10.1016/j.enbuild.2020.110445

Darwazeh et al., "Development of Inverse Greybox Model-Based Virtual Meters for Air Handling Units," 2021. https://doi.org/10.1109/TASE.2020.3005888

Zone anomaly detection function (zoneAnomaly.py)

Gunay and Shi, "Cluster analysis-based anomaly detection in building automation systems," 2020. https://doi.org/10.1016/j.enbuild.2020.110445

End-use disaggregation function (endUseDisaggregation.py)

Gunay et al., "Disaggregation of commercial building end-uses with automation system data," 2020. https://doi.org/10.1016/j.enbuild.2020.110222

Darwazeh et al., "Virtual metering of heat supplied by hydronic perimeter heaters in variable air volume zones," 2020. https://doi.org/10.1145/3427771.3429389

Hot/cold complaints analytics function (complaintsAnalytics.py)

Dutta et al., "A method for extracting performance metrics using work-order data,", 2020. https://doi.org/10.1080/23744731.2019.1693208

Occupancy count estimation function (occupancy.py)

Hobson et al., "Clustering and motif identification for occupancy-centric control of an air handling unit," 2020. https://doi.org/10.1016/j.enbuild.2020.110179

Gunay et al., "The effect of zone level occupancy characteristics on adaptive controls," 2017. https://www.researchgate.net/profile/Burak-Gunay/publication/319041337_The_effect_of_zone_level_occupancy_characteristics_on_adaptive_controls/links/598c5e9e0f7e9b07d224ddb6/The-effect-of-zone-level-occupancy-characteristics-on-adaptive-controls.pdf