WindOps AI

Почасовой прогноз нормализованной мощности двух ветротурбин на 24–48 часов по архивной погоде NOAA GFS.

Доведённый продукт

AI-агенты HackAlem ML Дашборды Энергетика

GitHub

Команда «hustlers» 8💬 0 комментариевс 23 сентября 2026 г.

О проекте

Команда: hustlers
Кейс HackAlem.ai: Энергетика — Самрук-Казына
Стадия: Доведённый продукт

Задача кейса: AI-агент, прогнозирующий выработку ветровой электростанции по погодным данным

Суть

Почасовой прогноз нормализованной мощности двух ветротурбин на 24–48 часов по архивной погоде NOAA GFS. Модели CatBoost, LLM-агент управляет расчётом через инструменты, есть replay исторических выпусков за февраль 2026 и паспорт происхождения данных.

Как устроено

Погодный модуль выборочно скачивает поля GFS 0,25° через HTTP Range, читает GRIB2 через ecCodes и кэширует точки двух площадок. Для каждой турбины обучена отдельная CatBoost-модель с погодными и геометрическими признаками (v2), январь отложен под backtest. Агент через OpenAI Responses API со строгими JSON-схемами вызывает 6 инструментов (list_weather_candidates, fetch_weather, validate_weather, predict_power, analyse_forecast, publish_forecast), есть и детерминированный режим. Результат проверяется (cutoff, полнота сетки, диапазон 0–1) и показывается в Streamlit с экспортом CSV и JSON-паспорта.

Ключевые решения

CatBoost
NOAA GFS GRIB2
ecCodes
OpenAI Responses API function calling
jsonschema-валидация инструментов
backtest
persistence baseline
replay выпусков

Метрики

v2 vs v1 на январе: MAE снижена на 3.76% / 4.32%, улучшены MAE и RMSE на обоих горизонтах; 162–175 тестов пройдено; 1344 февральских прогноза

Особенности

Каждый целевой час проверяется на историческую доступность погоды на момент запуска, чтобы не подсматривать будущее. Январские проверочные модели и финальные февральские разделены по cutoff. LLM только оркестрирует инструменты, числа выдаёт отдельный ML-плагин. Сохранён паспорт живого LLM-прогона: 6 успешных вызовов инструментов.

Стек

Python 3.12 · Streamlit · Plotly · pandas · NumPy · CatBoost · ecCodes · pytest · Playwright

Модели

OpenAI Responses API (модель через OPENAI_MODEL)

Проект создан за 5 часов на финале хакатона HackAlem.ai (23 сентября 2026, Астана). Код — BAITC-Hacks/hack-96839d9e-hustlers.

README

README.md

WindOps AI

Почасовой прогноз мощности ветроэлектростанции с проверкой происхождения данных.

Проект команды для HackAlem AI помогает диспетчеру и аналитику ВЭС работать с прогнозами двух турбин на 24–48 часов: видеть ожидаемую нормализованную мощность, погодные входы, историю выпусков и изменения после обновления погоды. Кейс предусматривает воспроизведение исторических запусков на февраль 2026 года.

Текущее состояние: реализованы интерфейс, архив NOAA GFS, backend и ML-плагин windops.ml.plugin. Данные, веса моделей, январская проверка и февральские прогнозы включены в репозиторий. Сквозной сценарий повторно проверен локально и в браузере; 162 теста прошли без пропусков. После настройки API сохранён отдельный LLM-запуск: одна турбина, 48 часов, исполнитель openai_responses, шесть успешных вызовов инструментов. Его паспорт и журнал проверены; отчёт и границы проверки.

Что реализовано

Финальная ML-версия: geometry v2

После указанной выше приёмки v1 добавлены погодные признаки и отдельно сохранены улучшенные модели. На уже просмотренном январе MAE снизилась на 3.76% / 4.32%; MAE и RMSE улучшились на обоих горизонтах. Это разработочное сравнение, а исходный независимый backtest v1 сохранён. Для v2 прошли 175 тестов и реальный браузерный сценарий. Отчёт команды о живом LLM выше относится к исходной v1; v2 проверялась в детерминированном режиме.

После установки зависимостей ниже рекомендуемый запуск на Linux/macOS:

bash scripts/run_improved.sh

Скрипт подготавливает локальные пути к включённому погодному кэшу и запускает существующее приложение. Готовые веса находятся в models/geometry_v2/active/; данные v2 — в data/experiments/ml_weather_geometry_v1/runtime_data/. ml/january_backtest.json внутри этого каталога содержит явно помеченное разработочное сравнение, ml/february_hourly.csv — новые 1344 прогноза февраля. Старые пути data/ml/ и models/<site_id>/ ниже относятся к сохранённой v1. Подробности, команды для обеих версий и откат: MODEL_IMPROVEMENT.md.

Возможности приложения

Интерфейс на русском языке: выбор одной или двух турбин, даты выпуска и горизонта; графики, почасовые таблицы, история выпусков, паспорт данных и журнал событий.
Архивная погода: загрузка выбранных полей оригинальных GFS, извлечение двух площадок, возобновляемый файловый кэш и CSV для обучения модели.
Обученные модели: отдельные CatBoost для двух турбин, сохранённые веса и паспорта; январские проверочные модели отделены от финальных февральских. Повторное обучение для запуска не требуется.
Проверки входов и результата: историческая доступность погоды, полнота часовой сетки, контрольные суммы, совместимость ML-модели, допустимый cutoff обучения и диапазон мощности 0–1.
Управление расчётом: один агент через OpenAI Responses API со строгими схемами инструментов; отдельный детерминированный режим и явно обозначенный резервный режим.
Replay и версии: последовательные исторические запуски, обнаружение нового допустимого погодного цикла, пересчёт и сохранение связи с предыдущим прогнозом. Эти сценарии мощности используют ML-плагин и требуют сохранённых весов моделей.
Проверка качества: загрузка январского backtest в интерфейс, расчёт MAE/RMSE и сравнение с baseline на совпадающих парах. Реальный артефакт — data/ml/january_backtest.json.
Экспорт: CSV выпуска, JSON-паспорт, очищенный журнал и выгрузки replay. Итоговый экспорт проходит дополнительные проверки; синтетические и самостоятельно загруженные непроверенные данные не получают права итоговой выгрузки.

Как работает решение

Основной пользовательский сценарий при наличии сохранённых моделей:

Пользователь выбирает турбины, исторический момент запуска и 24 либо 48 часов.
Backend получает подходящий GFS-выпуск из архива или кэша и проверяет доступность каждого целевого часа на момент запуска.
ML-модуль подготавливает признаки и рассчитывает нормализованную мощность. Его версия и обучающий cutoff проверяются перед вызовом.
Backend проверяет результат, вычисляет агрегаты, сохраняет прогноз, паспорта и журнал.
Интерфейс показывает график, таблицу и происхождение данных; проверенный результат доступен для экспорта.
Проверка обновлений при обнаружении нового допустимого цикла запускает новый расчёт. Старый выпуск сохраняется, сравнение проводится по общим целевым часам.

Для нового расчёта задайте WINDOPS_ML_MODULE=windops.ml.plugin; папка models/ уже присутствует в репозитории. Без настройки возникает MODEL_NOT_CONFIGURED, без подходящих весов — ML_MODEL_MISSING. Просмотр сохранённых результатов, погодный сборщик и тест интерфейса доступны независимо от весов.

Технологии

Версии зафиксированы в requirements*.txt:

Python — backend, подготовка данных и проверки. Погодный backend проверен на Python 3.12 / Linux; сохранённые результаты обучения участника №1 получены на Python 3.14.7 / Linux.
Streamlit 1.64.0 — веб-интерфейс.
Plotly 6.9.0 — интерактивные графики.
pandas 2.3.3, NumPy 2.3.5, tzdata 2026.4 — таблицы, вычисления и часовые пояса.
requests 2.34.2, ecCodes 2.48.0 — выборочные HTTP Range-запросы и чтение GRIB2.
CatBoost 1.2.10 — отдельные CPU-модели мощности двух турбин; дополнительно реализованы постоянный и ветровой baseline.
OpenAI Python SDK 3.19.0, Responses API — управляющий LLM-цикл. Конкретная LLM задаётся через OPENAI_MODEL; фиксированной модели по умолчанию нет.
jsonschema 4.26.0 — проверка аргументов инструментов.
pytest 9.1.1, Streamlit AppTest — автоматические проверки; для дополнительной браузерной приёмки предусмотрены Playwright и Chromium.
NOAA GFS 0,25° в публичном S3-архиве — внешние прогнозы погоды.

LLM управляет вызовами инструментов; численные прогнозы возвращает отдельный ML-модуль. Январские проверочные модели и финальные февральские модели имеют разные cutoff и версии.

Архитектура

Приложение состоит из Python-модулей в одном проекте; результаты хранятся в файлах.

flowchart TD
    UI["Streamlit: запрос и просмотр результата"] --> API["backend.py: запуск, replay, обновления"]
    CLI["CLI: сбор архива и запуск"] --> API
    CLI --> W["weather.py: NOAA GFS, кэш, проверки"]
    API --> A["agent.py: Responses API или детерминированный режим"]
    A --> P["pipeline.py: проверенные операции и сохранение"]
    P --> W
    P --> M["ml_bridge.py: контракт модели"]
    M --> ML["ml/: признаки и сохранённые CatBoost-модели"]
    P --> F["Файлы: прогноз, версии, паспорта, журнал"]
    F --> UI

Агенту доступны шесть инструментов: list_weather_candidates, fetch_weather, validate_weather, predict_power, analyse_forecast, publish_forecast. В коде ограничены число итераций и набор допустимых аргументов. Проверки повторяются внутри операций прогнозирования и сохранения, поэтому отдельный пропуск validate_weather не отменяет контроль данных.

app.py                         Интерфейс и пользовательские действия
src/windops/
  core.py                      Время, координаты, ошибки, контрольные суммы
  weather.py                   Загрузка, извлечение точек и проверка GFS
  cli.py                       Аудит CSV, сбор архива, запуск из терминала
  ml_bridge.py                 Подключение и проверка ML-модуля
  ml/                          Подготовка SCADA, обучение, backtest, плагин
  pipeline.py                  Расчётный процесс, сохранение, сравнение
  agent.py                     Строгие инструменты и цикл Responses API
  backend.py                   run_forecast, run_replay, check_updates
  ui/                          Графики, состояние, контракты и экспорт
tests/                         Тесты backend и интерфейса
scripts/browser_acceptance.py   Дополнительная браузерная приёмка
docs/                          Контракты, сценарии и подробные инструкции
data/                          Исходные CSV, погодный кэш и отчёты
models/                        Веса моделей, паспорта и контрольные суммы

Установка и запуск

Инструкции ниже предназначены для Windows x64 (PowerShell), macOS (Terminal, bash/zsh) и Linux (bash/zsh) с 64-разрядным Python 3.12. GPU для сохранённых моделей не нужен.

Статус совместимости: полный запуск проверен на Linux с Python 3.12.13. Windows и macOS пока не прошли отдельную приёмку проекта; наличие команд для них не означает гарантии работы на любой версии ОС или процессоре. CatBoost 1.2.10 предоставляет пакеты Python 3.12 для Windows x64, Linux и macOS Intel/Apple Silicon; ecCodes поставляет необходимую бинарную библиотеку через свои Python-пакеты. См. файлы CatBoost и установку ecCodes. Windows ARM и 32-разрядный Python в сценарий приёмки не входят.

Если проект ещё не скачан, установите Git и получите репозиторий:

git clone https://github.com/BAITC-Hacks/hack-96839d9e-hustlers.git
cd hack-96839d9e-hustlers

Для закрытого репозитория необходим доступ к нему. При скачивании ZIP распакуйте архив и откройте терминал в папке, содержащей app.py. Во всех командах ниже текущая папка — корень проекта. После установки зависимостей офлайн-сценарий использует включённые data/ и models/.

1. Подготовить окружение

Для установки зависимостей нужен интернет. Если .venv уже создана для этого проекта и этой ОС, повторять её создание не требуется. Папку .venv с другого компьютера переносить не нужно: создайте окружение заново в своём checkout.

Windows — PowerShell, при установленном Python 3.12 x64:

py -3.12 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements-dev.txt -r requirements-ml.txt

macOS и Linux — Terminal, при установленном Python 3.12:

python3.12 -m venv .venv
.venv/bin/python -m pip install -r requirements-dev.txt -r requirements-ml.txt

Если python3.12 или py -3.12 не найден, установите Python 3.12 либо воспользуйтесь уже установленным uv. Вариант macOS/Linux с uv:

uv venv --python 3.12.13 .venv
uv pip install --python .venv/bin/python -r requirements-dev.txt -r requirements-ml.txt

Вариант Windows с uv:

uv venv --python 3.12.13 .venv
uv pip install --python .venv\Scripts\python.exe -r requirements-dev.txt -r requirements-ml.txt

Активация окружения не требуется: команды напрямую используют Python из .venv. В macOS на Apple Silicon используйте Python той же архитектуры, что и устанавливаемые пакеты; не смешивайте окружения Intel и ARM.

requirements-dev.txt устанавливает UI, backend и pytest; requirements-ml.txt добавляет CatBoost для обучения и запуска моделей. Для запуска без тестов установите requirements-backend.txt и requirements-ml.txt. requirements-browser.txt нужен только для дополнительной проверки браузером. Воспроизведение исходного обучения требует отдельного окружения Python 3.14.7 и точных версий из data/ml/selection.json; команды приведены в docs/ML.md.

2. Настроить площадки

В config.example.json уже заполнены параметры двух турбин. При первом запуске создайте его локальную копию. Если у вас есть собственный config.local.json, сначала сравните настройки и сохраните нужные значения.

Windows — PowerShell:

if (-not (Test-Path config.local.json)) { Copy-Item config.example.json config.local.json }

macOS / Linux:

if [ ! -e config.local.json ]; then cp config.example.json config.local.json; fi

Содержимое конфигурации:

{
  "timezone": "Asia/Almaty",
  "timestamp_convention": "target_time = issued_at + horizon_step hours",
  "normalization": "0_1",
  "default_origin": "2026-01-31T23:00:00+05:00",
  "sites": [
    {"site_id": "turbine_1"},
    {"site_id": "turbine_2"}
  ]
}

На подготовленном рабочем месте команды этот файл уже существует. В Git он не включён. Без него доступен тест интерфейса на синтетике, но форма реального расчёта требует заполненных настроек.

3. Открыть интерфейс

Выполните блок для своей ОС в том же терминале. Переменные окружения действуют для запускаемого из него приложения.

Windows — PowerShell:

$env:PYTHONPATH = "src"
$env:WINDOPS_ML_MODULE = "windops.ml.plugin"
$env:WINDOPS_EXECUTION_MODE = "deterministic"
$env:WINDOPS_OFFLINE = "1"
.\.venv\Scripts\python.exe scripts/prepare_improved_runtime.py
$env:WINDOPS_MODEL_DIR = "$PWD/models/geometry_v2/active"
$env:WINDOPS_DATA_DIR = "$PWD/data/experiments/ml_weather_geometry_v1/runtime_data"
.\.venv\Scripts\python.exe -m streamlit run app.py

macOS / Linux:

export PYTHONPATH=src
export WINDOPS_ML_MODULE=windops.ml.plugin
export WINDOPS_EXECUTION_MODE=deterministic
export WINDOPS_OFFLINE=1
.venv/bin/python scripts/prepare_improved_runtime.py
export WINDOPS_MODEL_DIR="$PWD/models/geometry_v2/active"
export WINDOPS_DATA_DIR="$PWD/data/experiments/ml_weather_geometry_v1/runtime_data"
.venv/bin/python -m streamlit run app.py

Откройте локальное приложение. Эти настройки запускают реальные сохранённые модели с погодным кэшем, без вызова LLM. Для просмотра синтетического примера после установки зависимостей не нужны исходные датасеты, ML-модель, OpenAI-ключ или доступ к NOAA.

Проверить установку на своём компьютере

При ошибке запуска или перед демонстрацией остановите сервер через Ctrl+C и выполните проверки в том же терминале с переменными из шага 3.

Windows — PowerShell:

.\.venv\Scripts\python.exe -m eccodes selfcheck
.\.venv\Scripts\python.exe -c "import catboost, pandas, streamlit; print('Imports OK')"
.\.venv\Scripts\python.exe -m windops.cli forecast --site turbine_1 --origin 2026-01-31T23:00:00+05:00 --horizon 48

macOS / Linux:

.venv/bin/python -m eccodes selfcheck
.venv/bin/python -c "import catboost, pandas, streamlit; print('Imports OK')"
.venv/bin/python -m windops.cli forecast --site turbine_1 --origin 2026-01-31T23:00:00+05:00 --horizon 48

Ожидаются успешный selfcheck, Imports OK и JSON с forecast_id и rows: 48. После этого повторите запуск Streamlit из шага 3 и основной сценарий жюри ниже. Прохождение этих проверок подтверждает установку на конкретном компьютере, но не заменяет полный набор тестов.

Если установка сообщает No matching distribution found, проверьте версию и разрядность Python; не меняйте зафиксированные версии библиотек наугад. Для ошибки загрузки ecCodes используйте его selfcheck и инструкцию разработчика.

4. Переменные окружения

Шаблон имён — .env.example. Файл .env автоматически не читается: задавайте значения в окружении процесса.

WINDOPS_CONFIG — JSON-конфигурация интерфейса; по умолчанию config.local.json.
WINDOPS_BACKEND_MODULE — необязательная замена backend; приложение использует windops.backend.
WINDOPS_DATA_DIR — каталог backend-данных; по умолчанию data/ в корне репозитория.
WINDOPS_ML_MODULE=windops.ml.plugin — реализованный ML-плагин.
WINDOPS_MODEL_DIR — необязательный каталог сохранённых моделей; по умолчанию models/.
WINDOPS_EXECUTION_MODE — deterministic, agent или auto.
WINDOPS_OFFLINE=1 — запрет погодных сетевых запросов при запуске прогнозов через backend. Для команд weather и collect используйте флаг --offline.
OPENAI_API_KEY, OPENAI_MODEL — ключ проекта и идентификатор доступной ему модели для режима agent.
WINDOPS_SMOKE_BUNDLE — путь к настоящему сохранённому прогнозу для дополнительного интеграционного теста.

В режиме deterministic LLM не вызывается. auto при отсутствии ключа или ошибке LLM может перейти в явно обозначенный deterministic_fallback; этот режим всё равно требует настоящую ML-модель. Наличие ключа при незаданном OPENAI_MODEL является ошибкой конфигурации.

ML-модуль должен предоставлять get_model_metadata(site_id, forecast_origin) и predict_power(site_id, weather_rows, model_version). Точные требования — в контракте backend.

Как проверить решение жюри

Основной сценарий выполняется в браузере одинаково на всех ОС. Дополнительные CLI-примеры далее записаны для macOS/Linux; в PowerShell используйте .\.venv\Scripts\python.exe, а вместо export ИМЯ=значение — $env:ИМЯ = "значение".

Основной сценарий: реальные модели и данные

После установки и запуска приложения по инструкции выше:

Оставьте синтетику выключенной. Выберите обе турбины, 31.01.2026 23:00 Asia/Almaty, 48 часов и нажмите «Сформировать прогноз». Ожидаются график и 96 строк за 1–2 февраля.
Скачайте «CSV выбранного выпуска». На вкладке «Агент и данные» проверьте источник погоды, версию модели и журнал. В основном режиме исполнитель — deterministic.
На вкладке «Проверка качества» загрузите data/ml/january_backtest.json, нажмите «Проверить артефакт» и сравните MAE/RMSE с результатами ниже.
Для исходного выпуска нажмите «Проверить обновления» на вкладке «Агент и данные»: новый допустимый GFS создаёт связанную версию, предыдущий прогноз остаётся в истории.
Вернитесь на вкладку «Прогноз», раскройте «Исторический replay февраля» и запустите период 31.01.2026–28.02.2026. Ожидаются 58 выпусков без ошибок, полная выгрузка 2 784 строк. Отметьте предложенное правило выбора выпуска и скачайте «Февраль · один прогноз на час»: 1 344 уникальные пары «турбина — час».

Дата 31 января нужна для прогноза первых часов 1 февраля. Этот сценарий использует готовый погодный кэш и после установки не требует сети или API-ключа. Для показа LLM нужен отдельный режим agent, описанный ниже.

Сценарий А: интерфейс без внешних сервисов

Запустите приложение и включите «Тест интерфейса · синтетика» в боковой панели.
Выберите обе турбины, горизонт 48 часов и нажмите «Загрузить тестовый выпуск».
Откройте график, почасовую таблицу и вкладку «Агент и данные». Посмотрите паспорт и диагностическую выгрузку.
Убедитесь, что данные помечены как тестовые, а итоговый экспорт заблокирован. Тестовая зона времени — UTC; реальные площадки используют Asia/Almaty.

Этот сценарий проверяет интерфейс и его ограничения; качество ML и реальные действия LLM он не демонстрирует.

Сценарий Б: настоящая архивная погода

В отдельном терминале из корня проекта:

export PYTHONPATH=src
.venv/bin/python -m windops.cli weather --origin 2026-01-31T23:00:00+05:00 --horizon 48
.venv/bin/python -m windops.cli weather --origin 2026-01-31T23:00:00+05:00 --horizon 48 --offline

Первый вызов использует проверенный кэш или скачивает данные NOAA; без кэша необходим интернет. Ожидаются 96 строк: две турбины × 48 часов, версия погоды и метаданные доступности. Повтор без сети должен использовать тот же weather_version. Набор сохраняется в data/weather/bundles/.

Проверка запрета будущего погодного выпуска:

.venv/bin/python -m windops.cli weather --origin 2026-01-31T23:00:00+05:00 --run 2026-02-01T00:00:00Z --offline

Ожидается ошибка FUTURE_WEATHER и ненулевой код завершения: выбранный цикл инициализирован после исторического запуска. Это ожидаемый результат проверки.

Сценарий В: реальные сохранённые результаты и новый расчёт

Уже доступны без повторного обучения:

data/ml/january_backtest.json — загрузите его на вкладке «Проверка качества» и нажмите «Проверить артефакт»;
data/ml/february_hourly.csv — 1 344 уникальные строки, по одной на местный час февраля и турбину;
data/ml/february_full_releases.csv — 2 784 строки всех 58 исходных 48-часовых выпусков, включая март;
data/ml/run_summary.json — версии, объёмы обучения, метрики и ограничения.

Для нового расчёта используйте включённую в репозиторий папку models/: веса, паспорта и контрольные суммы находятся в models/<site_id>/<model_version>/. Повторное обучение для запуска не требуется. Команды воспроизводимого обучения — в docs/ML.md; они требуют указанного там окружения. Не редактируйте зафиксированный выбор параметров ради обхода проверки версий.

export PYTHONPATH=src
export WINDOPS_ML_MODULE=windops.ml.plugin
export WINDOPS_EXECUTION_MODE=deterministic
export WINDOPS_OFFLINE=1
.venv/bin/python -m windops.cli forecast --site turbine_1 --origin 2026-01-31T23:00:00+05:00 --horizon 48
.venv/bin/python -m streamlit run app.py

При успешном расчёте backend возвращает forecast_id и сохраняет bundle.json, forecast.csv, паспорта, контрольную сумму, отчёт проверки и events.jsonl в data/forecasts/<forecast_id>/.

Для повторного исторического replay при наличии весов:

.venv/bin/python -m windops.cli replay --start 2026-01-31 --end 2026-02-28
.venv/bin/python -m windops.ml.cli export-february

Даты означают местные даты выпусков в 23:00, включительно. Полные 48-часовые выпуски сохраняют часы марта. ML-выгрузка на февраль использует фиксированное правило: для дня D берутся шаги 1–24 исходного выпуска D−1 в 23:00. Внутридневные обновления в эту таблицу не включаются.

Для проверки обновления используйте check-updates <реальный_forecast_id> --as-of 2026-02-01T06:00:00Z для первого выпуска от 31 января. Без явного as_of UI сдвигает виртуальный момент на 12 часов.

Для LLM-сценария дополнительно задайте OPENAI_API_KEY, OPENAI_MODEL и WINDOPS_EXECUTION_MODE=agent в окружении процесса, затем повторите команду forecast. WINDOPS_OFFLINE=1 оставляет погоду в кэше; для API нужен интернет. При успешном новом запуске CLI возвращает executor: openai_responses. Используйте отдельный WINDOPS_DATA_DIR с копией data/weather/ и без старых прогнозов: повтор уже сохранённого выпуска сохраняет его первоначальный паспорт и исполнителя. Порядок проверки — в DEMO.md.

Проверенный локальный LLM-результат: turbine_1, выпуск от 31.01.2026 23:00 +05:00, 48 строк, шесть успешных инструментов от выбора погоды до сохранения. Проверены контрольные суммы, погодная сетка и историческая доступность, загрузка ML-модели, диапазон прогноза и совпадение с детерминированным расчётом. Отчёт проверки сохранённого запуска содержит журнал и ограничения. Старый data/ml/backend_verification.json относится к предшествующей детерминированной приёмке.

Результаты исходной независимой январской проверки v1

По сохранённым data/ml/run_summary.json и data/ml/january_backtest.json, на одинаковых 1 464 прогнозных парах каждой турбины:

Турбина 1: CatBoost MAE 0,215566, RMSE 0,299051; ветровой baseline MAE 0,239332, RMSE 0,332403. Улучшение MAE — 9,93%.
Турбина 2: CatBoost MAE 0,220588, RMSE 0,306425; ветровой baseline MAE 0,245854, RMSE 0,341828. Улучшение MAE — 10,28%.

Это ошибки в шкале нормализованной мощности 0–1. Они относятся к отдельным январским проверочным моделям, не к финальным моделям после переобучения на январе. Разбивка по горизонтам и сравнение с постоянным baseline — в docs/ML.md. Февральских метрик нет: фактическая мощность не предоставлена.

Выбраны CatBoost depth=4, 93 дерева для turbine_1 и 92 для turbine_2. Финальное обучение использовало 5 754 / 5 742 прогнозные пары, или 2 889 / 2 883 уникальных целевых часа. Январский cutoff — 31.12.2025 22:00 +05:00, финальный — 31.01.2026 22:00 +05:00.

Автоматические проверки

Из корня проекта после установки зависимостей. Переменные ниже включают реальные интеграционные проверки:

export PYTHONPATH=src
export WINDOPS_DATA_DIR="$PWD/data"
export WINDOPS_MODEL_DIR="$PWD/models"
export WINDOPS_ML_MODULE=windops.ml.plugin
export WINDOPS_EXECUTION_MODE=deterministic
export WINDOPS_OFFLINE=1
export WINDOPS_RUN_REAL_ML_TEST=1
export WINDOPS_SMOKE_BUNDLE="${WINDOPS_DATA_DIR:-data}/forecasts/forecast-3bd7a5659d75a23ee6b9aea4/bundle.json"
.venv/bin/python -m pytest -q
.venv/bin/python -m compileall -q app.py src
uv pip check --python .venv/bin/python

При установке через pip вместо последней команды используйте .venv/bin/python -m pip check.

Интеграционные проверки могут обновлять служебные метаданные и отчёты в каталоге данных. Для проверки на отдельной копии перенаправьте WINDOPS_DATA_DIR на полную копию data/; одного погодного кэша для всех тестов недостаточно.

162 теста без пропусков повторно пройдены на рабочем месте участника №2 с настоящими моделями, погодным кэшем и сохранённым прогнозом. Запуск и интеграция проверены на Python 3.12.13; исходное обучение выполнялось на Python 3.14.7. Для полного повторения нужны WINDOPS_RUN_REAL_ML_TEST=1 и WINDOPS_SMOKE_BUNDLE, указывающий на существующий локальный bundle.json. Абсолютные пути в перенесённых отчётах относятся к компьютеру автора: используйте путь внутри своего checkout. Проверка выполнена с отдельной копией data/ через WINDOPS_DATA_DIR, чтобы не обновлять исходные артефакты. Без весов реальная ML-проверка пропускается; без необходимых погодных файлов пропускаются и тесты локального GFS. В модульных тестах OpenAI заменяется тестовым клиентом.

Подробная демонстрация и дополнительная приёмка Chromium описаны в docs/DEMO.md. Тесты не являются доказательством точности прогноза мощности.

Данные и интеграции

Наблюдения турбин

Локальные data/scada/turbine_1.csv и turbine_2.csv — неизменённые копии файлов организаторов. Они содержат 10-минутные измерения с марта 2023 года по 31 января 2026 года: ветер, температуру и нормализованную мощность. В исходных временных рядах есть пропуски; аудит доступен через:

.venv/bin/python -m windops.cli audit

Команда требует обеих локальных CSV-копий и сохраняет data/reports/scada_audit.json. Мощность имеет шкалу 0–1; единицы ветра — м/с, температуры — °C. Фактических значений мощности февраля нет.

Погода NOAA GFS

Загрузчик обращается к https://noaa-gfs-bdp-pds.s3.amazonaws.com по HTTPS без AWS-ключа. Он читает индексы и выбранные GRIB2-сообщения температуры на 2 м и компонент ветра на 10/100 м, затем извлекает точки:

turbine_1: 43.645150, 78.535604;
turbine_2: 43.643198, 78.538828.

Обе площадки используют ближайший узел GFS 43.75, 78.5, примерно в 12 км от турбин. Ветер на 100 м не объявляется ветром на высоте ступицы.

Исторический допуск требует run_initialized_at <= availability_upper_bound <= forecast_origin для каждого целевого часа. Верхняя граница берётся из Last-Modified оригинального объекта S3: она относится к данной версии объекта, а не к точному первоначальному времени публикации NOAA. Время сегодняшнего скачивания хранится отдельно. Контролируются HTTP 206, диапазоны байтов, ETag и SHA-256.

Внутри backend используется UTC, в реальном интерфейсе — Asia/Almaty. Для 2025–2026 годов местные 23:00 соответствуют 18:00 UTC. Ежедневный сбор использует цикл GFS 00 UTC; первый целевой час — через час после пользовательского origin.

Подтверждённый локальный архив: 152 ежедневных выпуска с 30.09.2025 по 28.02.2026, по 48 часов для двух турбин, 14 592 строки в data/weather/weather_for_ml.csv. Отчёты data/reports/weather_coverage.json и backend_verification.json фиксируют покрытие, контрольные суммы и успешную повторную проверку без сети. Это начальный диапазон, а не вся история с 2023 года.

Воспроизводимый сбор полного диапазона:

.venv/bin/python -m windops.cli collect --start 2025-09-30 --end 2026-02-28 --workers 8
.venv/bin/python -m windops.cli export-weather

Полный сбор требует десятков ГБ сетевого трафика; для приёмки достаточно одного выпуска из сценария Б. Кэш сохраняет извлечённые точки и свидетельства, а не полные глобальные сетки. Повторный сбор использует уже загруженные объекты.

Ключ таблицы — (site_id, forecast_origin, target_time). Один целевой час может встречаться в нескольких выпусках с разной заблаговременностью.

OpenAI и локальное хранение

Управляющий код отправляет в OpenAI параметры запроса, идентификаторы наборов, статусы и агрегаты. Исходные CSV и полные погодные ряды ему не передаются; запросы используют store=False. Секреты берутся из окружения.

В текущей версии подготовленные файлы data/ уже отслеживаются Git: в них входят обе исходные CSV-копии, погодный архив и отчёты. При передаче только исходного кода каталог данных нужно предоставить отдельно, а погодный архив можно восстановить загрузчиком.

Для передачи готовой версии включены исходные модели и проверенные артефакты models/geometry_v2/. Остальные новые экспериментальные модели игнорируются. .env, config.local.json, artifacts/, локальные ссылки и пересоздаваемый кэш прогнозов v2 не включаются в Git. Подробности backend — в docs/BACKEND.md; фактический состав данных сверяйте с Git, поскольку прежние заметки описывают первоначально локальное хранение.

Ограничения текущей версии

Приёмка ОС: полный сценарий проверен на Linux. Для Windows x64 и macOS приведены команды установки и локальной проверки, но отдельных результатов запуска проекта на этих ОС пока нет.
Окружение обучения зафиксировано: готовые веса включены в Git; для повторения выбора параметров используются точные версии из data/ml/selection.json. Для нового прогноза нужны ML-зависимости и настройка WINDOPS_ML_MODULE.
Погода имеет пространственное ограничение: один узел GFS обслуживает обе турбины; отдельной локальной коррекции погоды пока нет.

Развёрнутая версия

Подтверждённая публичная ссылка на deployed-версию в репозитории отсутствует. Доступен локальный запуск по адресу http://localhost:8501; это адрес на компьютере, где запущен Streamlit.

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Структура репозитория (3000 файлов)

.env.example
.gitignore
.streamlit/config.toml
AGENTS.md
README.md
app.py
config.example.json
data/README.md
data/experiments/ml_guarded_v1/comparison.json
data/experiments/ml_guarded_v1/comparison_checksum.json
data/experiments/ml_guarded_v1/december_predictions/turbine_1_00.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_01.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_02.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_03.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_04.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_05.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_06.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_07.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_08.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_09.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_10.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_1_11.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_00.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_01.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_02.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_03.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_04.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_05.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_06.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_07.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_08.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_09.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_10.csv
data/experiments/ml_guarded_v1/december_predictions/turbine_2_11.csv
data/experiments/ml_guarded_v1/protocol.json
data/experiments/ml_guarded_v1/selection.json
data/experiments/ml_guarded_v1/selection_checksum.json
data/experiments/ml_weather_geometry_v1/activation.json
data/experiments/ml_weather_geometry_v1/build.json
data/experiments/ml_weather_geometry_v1/comparison.json
data/experiments/ml_weather_geometry_v1/comparison_checksum.json
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_00.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_01.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_02.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_03.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_04.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_1_05.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_00.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_01.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_02.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_03.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_04.csv
data/experiments/ml_weather_geometry_v1/december_predictions/turbine_2_05.csv
data/experiments/ml_weather_geometry_v1/january_predictions/turbine_1.csv
data/experiments/ml_weather_geometry_v1/january_predictions/turbine_2.csv
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