eensight
This Python package implements the measurement and verification (M&V) methodology that has been developed by the H2020 project SENSEI - Smart Energy Services to Improve the Energy Efficiency of the European Building Stock.
https://github.com/hebes-io/eensight
Category: Consumption
Sub Category: Buildings and Heating
Keywords
building-energy energy-data energy-efficiency kedro pipelines
Last synced: about 11 hours ago
JSON representation
Repository metadata
The measurement and verification methodology of the H2020 project SENSEI
- Host: GitHub
- URL: https://github.com/hebes-io/eensight
- Owner: hebes-io
- License: apache-2.0
- Created: 2020-05-16T16:56:19.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-22T11:56:03.000Z (over 2 years ago)
- Last Synced: 2024-10-29T19:57:26.331Z (6 months ago)
- Topics: building-energy, energy-data, energy-efficiency, kedro, pipelines
- Language: Python
- Homepage: https://hebes-io.github.io/rethinking/
- Size: 37.7 MB
- Stars: 16
- Watchers: 4
- Forks: 1
- Open Issues: 0
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
The eensight tool for measurement and verification of energy efficiency improvements
The eensight Python package implements the measurement and verification (M&V) methodology that has been developed by the H2020 project SENSEI - Smart Energy Services to Improve the Energy Efficiency of the European Building Stock.
The online book Rethinking Measurement and Verification of Energy Savings (accessible here) explains in detail both the methodology and its implementation.
Installation
eensight can be installed by pip:
pip install eensight
Usage
1. Through the command line
All the functionality in eensight is organized around data pipelines. Each pipeline consumes data and other artifacts (such as models) produced by a previous pipeline, and produces new data and artifacts for its successor pipelines.
There are four (4) pipelines in eensight. The names of the pipelines and the associations between pipelines and namespaces are summarized below:
| train | test | apply | |
|---|---|---|---|
| preprocess | ✔ | ✔ | ✔ |
| predict | ✔ | ✔ | ✔ |
| evaluate | ✔ | ✔ | |
| adjust | ✔ |
The primary way of using eensight is through the command line. The first argument is always the name of the pipeline to run, such as:
eensight run predict --namespace train
The command
eensight run --help
prints the documentation for all the options that can be passed to the command line.
2. As a library
The pipelines of eensight are separate from the methods that implement them, so that the latter can be used directly:
import pandas as pd
from eensight.methods.prediction.baseline import UsagePredictor
from eensight.methods.prediction.activity import estimate_activity
non_occ_features = ["temperature", "dew point temperature"]
activity = estimate_activity(
X,
y,
non_occ_features=non_occ_features,
exog="temperature",
assume_hurdle=False,
)
X_act = pd.concat([X, activity.to_frame("activity")], axis=1)
model = UsagePredictor(skip_calendar=True).fit(X_act, y)
Owner metadata
- Name: HEBES Intelligence
- Login: hebes-io
- Email:
- Kind: user
- Description:
- Website: www.hebes.io
- Location: Athens, Greece
- Twitter:
- Company: HEBES Intelligence
- Icon url: https://avatars.githubusercontent.com/u/48722900?u=309133f5f55fbb476025c4316e670e12691c0bd8&v=4
- Repositories: 3
- Last ynced at: 2023-04-04T18:17:29.884Z
- Profile URL: https://github.com/hebes-io
GitHub Events
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Last Year
Committers metadata
Last synced: 4 months ago
Total Commits: 119
Total Committers: 1
Avg Commits per committer: 119.0
Development Distribution Score (DDS): 0.0
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| Sotiris Papadelis | 4****o | 119 |
Committer domains:
Issue and Pull Request metadata
Last synced: 18 days ago
Total issues: 1
Total pull requests: 2
Average time to close issues: 10 months
Average time to close pull requests: 5 days
Total issue authors: 1
Total pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 1.0
Merged pull request: 1
Bot issues: 0
Bot pull requests: 1
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- samyoung-dsci (1)
Top Pull Request Authors
- hebes-io (1)
- dependabot[bot] (1)
Top Issue Labels
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- dependencies (1)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 140 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
pypi.org: eensight
A library for measurement and verification of energy efficiency projects.
- Homepage: https://github.com/hebes-io/eensight
- Documentation: https://eensight.readthedocs.io/
- Licenses: Apache License, Version 2.0
- Latest release: 1.0.2 (published over 2 years ago)
- Last Synced: 2025-04-26T12:01:15.557Z (1 day ago)
- Versions: 3
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 140 Last month
-
Rankings:
- Dependent packages count: 6.633%
- Stargazers count: 16.639%
- Average: 21.315%
- Downloads: 22.202%
- Forks count: 30.492%
- Dependent repos count: 30.611%
- Maintainers (1)
Dependencies
- PyYAML >=4.2,<6.0
- Unidecode >=1.2.0
- catboost >=1.0
- environs >=9.3.5
- feature-encoders >=0.2
- holidays >=0.11.2
- kedro ==0.17.5
- marshmallow >=3.13.0
- matplotlib >=3.4.3
- metric-learn >=0.6.2
- notebook >=6.4.4
- omegaconf >=2.1.0
- optuna >=2.9.1
- pandas >=1.3.3
- rich >=10.9.0
- scikit-learn >=1.0
- scipy >=1.7.1
- seaborn >=0.11.1
- stumpy >=1.9.2
Score: 7.72134861261795