| Name | stroomer JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | None |
| Summary | Energy inference and EV charging ETA utilities (SoC-based). |
| upload_time | 2025-08-20 09:31:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to do so, subject to the
following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
energy
iot
nilm
ev
charging
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# stroomer
<p align="center">
<img src="https://stroomer.co.id/images/logo/logo-stroom.png" alt="Stroomer" width="240"/>
</p>
<p align="center">
<a href="https://pypi.org/project/stroomer/">
<img src="https://img.shields.io/pypi/v/stroomer.svg" alt="PyPI">
</a>
<a href="https://pepy.tech/project/stroomer">
<img src="https://static.pepy.tech/badge/stroomer" alt="Downloads">
</a>
<a href="https://pypi.org/project/stroomer/">
<img src="https://img.shields.io/pypi/pyversions/stroomer.svg" alt="Python versions">
</a>
<a href="https://github.com/foldadjo/stroomer/blob/main/LICENSE">
<img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License">
</a>
<a href="https://github.com/foldadjo/stroomer">
<img src="https://img.shields.io/badge/types-typed-brightgreen.svg" alt="types: typed">
</a>
<a href="https://github.com/foldadjo/stroomer/issues/new?labels=question">
<img src="https://img.shields.io/badge/stackoverflow-Ask%20questions-ef8236.svg" alt="Ask questions">
</a>
</p>
NumPy-style quick links:
- **Website:** <https://stroomer.co.id>
- **Documentation:** <https://github.com/foldadjo/stroomer#readme>
- **PyPI:** <https://pypi.org/project/stroomer/>
- **Source code:** <https://github.com/foldadjo/stroomer>
- **Contributing:** <https://github.com/foldadjo/stroomer#kontribusi>
- **Bug reports:** <https://github.com/foldadjo/stroomer/issues>
- **Report a security vulnerability:** <https://github.com/foldadjo/stroomer/security/advisories/new>
---
Utilities untuk **appliance inference** (menebak perangkat yang menyala dari V, I, PF, P) dan **EV charging ETA** (berbasis SoC).
- **Catalog built-in**: tiap perangkat punya `watt`, `pf`, `type`, `phase`, `standby_w`, `surge_mult`.
- **Prediksi multi-fitur**: cocokkan **P**, **I**, **PF** (bukan power saja).
- **Standby-aware**: total daya = *standby* seluruh unit + kontribusi aktif unit ON.
- **SoC ETA**: prediksi durasi & waktu selesai charging berbasis kapasitas, target SoC, efisiensi.
Repo: **<https://github.com/foldadjo/stroomer>**
---
## Tabel Isi
- [Install](#install)
- [Quickstart](#quickstart)
- [Appliance inference](#appliance-inference)
- [EV charging ETA](#ev-charging-eta)
- [API Ringkas](#api-ringkas)
- [Konfigurasi & Tuning](#konfigurasi--tuning)
- [Catatan Akurasi](#catatan-akurasi)
- [Kontribusi](#kontribusi)
- [Lisensi](#lisensi)
---
## Install
Dari **PyPI**:
```bash
pip install stroomer
```
(Opsional) Dari **TestPyPI**:
```bash
pip install -i https://test.pypi.org/simple --extra-index-url https://pypi.org/simple stroomer
```
> Disarankan Python 3.9+.
---
## Quickstart
### Appliance inference
```python
from stroomer import StroomerPredictor
p = StroomerPredictor()
# Lihat daftar elektronik di katalog (nama → spesifikasi)
catalog = p.electronic_list()
# print(catalog)
# Daftarkan JUMLAH MAKSIMAL unit yang mungkin ADA (nama fleksibel/alias)
p.set_appliances({
"kipas": 5,
"lampu_18W": 5,
"lampu_32W": 5,
"lampu": 3,
"TV": 2,
"Kulkas": 2,
"AC": 1,
"ev_charger": 1,
"mesin_cuci": 1,
"chrger_motor": 3, # alias typo → otomatis dipetakan
})
# (Opsional) Sesuaikan katalog untuk site tertentu
p.configure_catalog({
"ev_charger": {"watt": 3200, "pf": 0.99},
})
# Snapshot meter
V, I, PF, P = 220, 6, 0.95, 2301 # jika P real tersedia, berikan (diprioritaskan)
out = p.predict(voltage=V, current=I, power_factor=PF, power=P)
print("Perangkat ON :", out["on"]) # contoh: {"lampu_18w": 3, "kipas_berdiri": 1}
print("Target :", out["target"]) # {"P":..., "I":..., "PF":..., "V":...}
print("Prediksi :", out["pred"]) # {"P":..., "I":..., "PF":...}
print("Loss :", out["loss"]) # skor gabungan (semakin kecil semakin baik)
print("RelErr P :", out["rel_error_P"])
```
> Jika `V*I*PF` dan `P` tidak konsisten (lumrah antar meter), model **memprioritaskan P** sambil tetap menilai I & PF untuk kombinasi yang masuk akal.
---
### EV charging ETA
```python
from stroomer import ChargingTimePredictor
eta = ChargingTimePredictor(
capacity_kwh=50, # kapasitas baterai
target_soc=90, # target SoC (%)
efficiency=0.92 # efisiensi pengisian
)
result = eta.predict(power=8000, SoC=30)
print(result)
# -> {"FinishDuration":"03:14","FinishTime":"2025-08-18T13:25:00+00:00"}
```
---
## API Ringkas
### `StroomerPredictor`
- `electronic_list() -> dict`
Katalog efektif (watt, pf, type, phase, standby_w, surge_mult).
- `configure_catalog(overrides: dict)`
Override sebagian atribut, mis. `{"ev_charger": {"watt": 3500, "pf": 0.99}}`.
- `set_appliances(counts: dict[str,int])`
Daftarkan jumlah **maksimal** unit per perangkat di lokasi (nama fleksibel/alias).
- `predict(voltage=None, current=None, power_factor=None, power=None, ...) -> dict`
Mengembalikan:
```python
{
"on": {"nama": jumlah_on, ...},
"target": {"P":..., "I":..., "PF":..., "V":...},
"pred": {"P":..., "I":..., "PF":...},
"loss": float, # skor gabungan
"rel_error_P": float # |P_pred - P_meas| / P_meas
}
```
### `ChargingTimePredictor`
- `__init__(capacity_kwh, target_soc=90.0, efficiency=0.92)`
- `predict(power: W, SoC: %) -> {"FinishDuration": "HH:MM", "FinishTime": ISO8601_UTC}`
---
## Konfigurasi & Tuning
- **Bobot loss**: menyeimbangkan pentingnya P, I, PF
```python
p.weights = {"P": 0.6, "I": 0.3, "PF": 0.1}
```
- **Tolerance pruning** (relatif di domain P)
```python
p.tolerance = 0.08 # 8% → pruning lebih agresif
```
- **Batas hard per perangkat**
```python
p.global_count_cap = 30
```
**Tips akurasi**
- Sesuaikan `pf` dan `watt` di katalog agar mendekati kondisi lapangan via `configure_catalog`.
- Berikan **P** dari meter jika tersedia; jika tidak, berikan kombinasi **V, I, PF** yang reliabel.
- `set_appliances` sebaiknya **upper bound** realistis, bukan jumlah pasti.
---
## Catatan Akurasi
- **PF tipikal** pada katalog adalah nilai rata-rata umum; silakan sesuaikan.
- **Standby-aware**: algoritma menghitung baseline *standby* + kontribusi aktif unit ON.
- Perbedaan instrumen/pipeline dapat membuat **P** dan **V·I·PF** tidak identik—model memadukan semuanya (dengan bobot).
---
## Kontribusi
Kontribusi sangat diterima 🙌
Buka **issue** atau **pull request** di:
**<https://github.com/foldadjo/stroomer>**
Untuk pengembangan lokal:
```bash
git clone https://github.com/foldadjo/stroomer.git
cd stroomer
pip install -U build twine pytest
pip install -e .
pytest -q
```
> Ikuti PEP8 dan tambahkan test untuk setiap fitur/perbaikan.
---
## Lisensi
MIT © Stroomer Team — lihat berkas `LICENSE`.
Raw data
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"description": "# stroomer\n\n<p align=\"center\">\n <img src=\"https://stroomer.co.id/images/logo/logo-stroom.png\" alt=\"Stroomer\" width=\"240\"/>\n</p>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/stroomer/\">\n <img src=\"https://img.shields.io/pypi/v/stroomer.svg\" alt=\"PyPI\">\n </a>\n <a href=\"https://pepy.tech/project/stroomer\">\n <img src=\"https://static.pepy.tech/badge/stroomer\" alt=\"Downloads\">\n </a>\n <a href=\"https://pypi.org/project/stroomer/\">\n <img src=\"https://img.shields.io/pypi/pyversions/stroomer.svg\" alt=\"Python versions\">\n </a>\n <a href=\"https://github.com/foldadjo/stroomer/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/badge/license-MIT-blue.svg\" alt=\"License\">\n </a>\n <a href=\"https://github.com/foldadjo/stroomer\">\n <img src=\"https://img.shields.io/badge/types-typed-brightgreen.svg\" alt=\"types: typed\">\n </a>\n <a href=\"https://github.com/foldadjo/stroomer/issues/new?labels=question\">\n <img src=\"https://img.shields.io/badge/stackoverflow-Ask%20questions-ef8236.svg\" alt=\"Ask questions\">\n </a>\n</p>\n\nNumPy-style quick links:\n\n- **Website:** <https://stroomer.co.id>\n- **Documentation:** <https://github.com/foldadjo/stroomer#readme>\n- **PyPI:** <https://pypi.org/project/stroomer/>\n- **Source code:** <https://github.com/foldadjo/stroomer>\n- **Contributing:** <https://github.com/foldadjo/stroomer#kontribusi>\n- **Bug reports:** <https://github.com/foldadjo/stroomer/issues>\n- **Report a security vulnerability:** <https://github.com/foldadjo/stroomer/security/advisories/new>\n\n---\n\nUtilities untuk **appliance inference** (menebak perangkat yang menyala dari V, I, PF, P) dan **EV charging ETA** (berbasis SoC).\n\n- **Catalog built-in**: tiap perangkat punya `watt`, `pf`, `type`, `phase`, `standby_w`, `surge_mult`.\n- **Prediksi multi-fitur**: cocokkan **P**, **I**, **PF** (bukan power saja).\n- **Standby-aware**: total daya = *standby* seluruh unit + kontribusi aktif unit ON.\n- **SoC ETA**: prediksi durasi & waktu selesai charging berbasis kapasitas, target SoC, efisiensi.\n\nRepo: **<https://github.com/foldadjo/stroomer>**\n\n---\n\n## Tabel Isi\n\n- [Install](#install)\n- [Quickstart](#quickstart)\n - [Appliance inference](#appliance-inference)\n - [EV charging ETA](#ev-charging-eta)\n- [API Ringkas](#api-ringkas)\n- [Konfigurasi & Tuning](#konfigurasi--tuning)\n- [Catatan Akurasi](#catatan-akurasi)\n- [Kontribusi](#kontribusi)\n- [Lisensi](#lisensi)\n\n---\n\n## Install\n\nDari **PyPI**:\n\n```bash\npip install stroomer\n```\n\n(Opsional) Dari **TestPyPI**:\n\n```bash\npip install -i https://test.pypi.org/simple --extra-index-url https://pypi.org/simple stroomer\n```\n\n> Disarankan Python 3.9+.\n\n---\n\n## Quickstart\n\n### Appliance inference\n\n```python\nfrom stroomer import StroomerPredictor\n\np = StroomerPredictor()\n\n# Lihat daftar elektronik di katalog (nama \u2192 spesifikasi)\ncatalog = p.electronic_list()\n# print(catalog)\n\n# Daftarkan JUMLAH MAKSIMAL unit yang mungkin ADA (nama fleksibel/alias)\np.set_appliances({\n \"kipas\": 5,\n \"lampu_18W\": 5,\n \"lampu_32W\": 5,\n \"lampu\": 3,\n \"TV\": 2,\n \"Kulkas\": 2,\n \"AC\": 1,\n \"ev_charger\": 1,\n \"mesin_cuci\": 1,\n \"chrger_motor\": 3, # alias typo \u2192 otomatis dipetakan\n})\n\n# (Opsional) Sesuaikan katalog untuk site tertentu\np.configure_catalog({\n \"ev_charger\": {\"watt\": 3200, \"pf\": 0.99},\n})\n\n# Snapshot meter\nV, I, PF, P = 220, 6, 0.95, 2301 # jika P real tersedia, berikan (diprioritaskan)\n\nout = p.predict(voltage=V, current=I, power_factor=PF, power=P)\n\nprint(\"Perangkat ON :\", out[\"on\"]) # contoh: {\"lampu_18w\": 3, \"kipas_berdiri\": 1}\nprint(\"Target :\", out[\"target\"]) # {\"P\":..., \"I\":..., \"PF\":..., \"V\":...}\nprint(\"Prediksi :\", out[\"pred\"]) # {\"P\":..., \"I\":..., \"PF\":...}\nprint(\"Loss :\", out[\"loss\"]) # skor gabungan (semakin kecil semakin baik)\nprint(\"RelErr P :\", out[\"rel_error_P\"])\n```\n\n> Jika `V*I*PF` dan `P` tidak konsisten (lumrah antar meter), model **memprioritaskan P** sambil tetap menilai I & PF untuk kombinasi yang masuk akal.\n\n---\n\n### EV charging ETA\n\n```python\nfrom stroomer import ChargingTimePredictor\n\neta = ChargingTimePredictor(\n capacity_kwh=50, # kapasitas baterai\n target_soc=90, # target SoC (%)\n efficiency=0.92 # efisiensi pengisian\n)\n\nresult = eta.predict(power=8000, SoC=30)\nprint(result)\n# -> {\"FinishDuration\":\"03:14\",\"FinishTime\":\"2025-08-18T13:25:00+00:00\"}\n```\n\n---\n\n## API Ringkas\n\n### `StroomerPredictor`\n\n- `electronic_list() -> dict` \n Katalog efektif (watt, pf, type, phase, standby_w, surge_mult).\n\n- `configure_catalog(overrides: dict)` \n Override sebagian atribut, mis. `{\"ev_charger\": {\"watt\": 3500, \"pf\": 0.99}}`.\n\n- `set_appliances(counts: dict[str,int])` \n Daftarkan jumlah **maksimal** unit per perangkat di lokasi (nama fleksibel/alias).\n\n- `predict(voltage=None, current=None, power_factor=None, power=None, ...) -> dict` \n Mengembalikan:\n\n ```python\n {\n \"on\": {\"nama\": jumlah_on, ...},\n \"target\": {\"P\":..., \"I\":..., \"PF\":..., \"V\":...},\n \"pred\": {\"P\":..., \"I\":..., \"PF\":...},\n \"loss\": float, # skor gabungan\n \"rel_error_P\": float # |P_pred - P_meas| / P_meas\n }\n ```\n\n### `ChargingTimePredictor`\n\n- `__init__(capacity_kwh, target_soc=90.0, efficiency=0.92)`\n- `predict(power: W, SoC: %) -> {\"FinishDuration\": \"HH:MM\", \"FinishTime\": ISO8601_UTC}`\n\n---\n\n## Konfigurasi & Tuning\n\n- **Bobot loss**: menyeimbangkan pentingnya P, I, PF \n\n ```python\n p.weights = {\"P\": 0.6, \"I\": 0.3, \"PF\": 0.1}\n ```\n\n- **Tolerance pruning** (relatif di domain P) \n\n ```python\n p.tolerance = 0.08 # 8% \u2192 pruning lebih agresif\n ```\n\n- **Batas hard per perangkat** \n\n ```python\n p.global_count_cap = 30\n ```\n\n**Tips akurasi**\n\n- Sesuaikan `pf` dan `watt` di katalog agar mendekati kondisi lapangan via `configure_catalog`.\n- Berikan **P** dari meter jika tersedia; jika tidak, berikan kombinasi **V, I, PF** yang reliabel.\n- `set_appliances` sebaiknya **upper bound** realistis, bukan jumlah pasti.\n\n---\n\n## Catatan Akurasi\n\n- **PF tipikal** pada katalog adalah nilai rata-rata umum; silakan sesuaikan.\n- **Standby-aware**: algoritma menghitung baseline *standby* + kontribusi aktif unit ON.\n- Perbedaan instrumen/pipeline dapat membuat **P** dan **V\u00b7I\u00b7PF** tidak identik\u2014model memadukan semuanya (dengan bobot).\n\n---\n\n## Kontribusi\n\nKontribusi sangat diterima \ud83d\ude4c \nBuka **issue** atau **pull request** di:\n\n**<https://github.com/foldadjo/stroomer>**\n\nUntuk pengembangan lokal:\n\n```bash\ngit clone https://github.com/foldadjo/stroomer.git\ncd stroomer\npip install -U build twine pytest\npip install -e .\npytest -q\n```\n\n> Ikuti PEP8 dan tambahkan test untuk setiap fitur/perbaikan.\n\n---\n\n## Lisensi\n\nMIT \u00a9 Stroomer Team \u2014 lihat berkas `LICENSE`.\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2025\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to do so, subject to the\n following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n ",
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