## 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](https://senseih2020.eu/).
The online book *Rethinking Measurement and Verification of Energy Savings* (accessible [here](https://hebes-io.github.io/rethinking/index.html)) explains in detail both the methodology and its implementation.
## Installation
`eensight` can be installed by pip:
```bash
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:
```bash
eensight run predict --namespace train
```
The command
```bash
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:
```python
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)
```
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"description": "## The `eensight` tool for measurement and verification of energy efficiency improvements\r\n\r\nThe `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](https://senseih2020.eu/). \r\n\r\nThe online book *Rethinking Measurement and Verification of Energy Savings* (accessible [here](https://hebes-io.github.io/rethinking/index.html)) explains in detail both the methodology and its implementation.\r\n\r\n## Installation\r\n\r\n`eensight` can be installed by pip:\r\n\r\n```bash\r\npip install eensight\r\n```\r\n\r\n## Usage\r\n\r\n### 1. Through the command line\r\n\r\nAll 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.\r\n\r\nThere are four (4) pipelines in `eensight`. The names of the pipelines and the associations between pipelines and namespaces are summarized below:\r\n\r\n| \t| train \t| test \t| apply |\r\n|------------\t|----------\t|----------\t|---------|\r\n| preprocess \t| ✔ \t| ✔ \t| ✔|\r\n| predict \t| ✔ \t| ✔\t| ✔|\r\n| evaluate \t| \t| ✔ | ✔|\r\n| adjust \t| \t| | ✔|\r\n\r\nThe primary way of using `eensight` is through the command line. The first argument is always the name of the pipeline to run, such as:\r\n\r\n```bash\r\neensight run predict --namespace train\r\n```\r\nThe command\r\n\r\n```bash\r\neensight run --help\r\n```\r\nprints the documentation for all the options that can be passed to the command line.\r\n\r\n### 2. As a library\r\n\r\nThe pipelines of `eensight` are separate from the methods that implement them, so that the latter can be used directly:\r\n\r\n```python\r\nimport pandas as pd\r\n\r\nfrom eensight.methods.prediction.baseline import UsagePredictor\r\nfrom eensight.methods.prediction.activity import estimate_activity\r\n\r\nnon_occ_features = [\"temperature\", \"dew point temperature\"]\r\n\r\nactivity = estimate_activity(\r\n X, \r\n y, \r\n non_occ_features=non_occ_features, \r\n exog=\"temperature\",\r\n assume_hurdle=False,\r\n\r\n)\r\n\r\nX_act = pd.concat([X, activity.to_frame(\"activity\")], axis=1)\r\nmodel = UsagePredictor(skip_calendar=True).fit(X_act, y)\r\n```\r\n\r\n",
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