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WindAgent

Агент ежедневного выпуска почасового вероятностного прогноза (P10/P50/P90) мощности двух турбин ВЭС на 48 ч с пересчётом при выходе нового рана…

Доведённый продукт
AI-агентыHackAlemMLДашбордыЭнергетика
GitHub
Команда «localhosters» 19💬 0 комментариевс 23 сентября 2026 г.

О проекте

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

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

Суть

Агент ежедневного выпуска почасового вероятностного прогноза (P10/P50/P90) мощности двух турбин ВЭС на 48 ч с пересчётом при выходе нового рана погоды, отчётом диспетчеру и интерактивным дашбордом.

Как устроено

Архив прогнозов Open-Meteo Single/Previous Runs (ECMWF IFS 9 км) и SCADA (10 мин → 1 ч) идут в модель: v0 — линейный MOS ветра на 100 м + логистическая кривая мощности, v1 — LightGBM, P50 = 0.4·LightGBM + 0.6·v0, P10/P90 — эмпирические квантили остатков. Собственный runtime run_issue из 11 шагов (clock, fetch_nwp, data quality, predict, compare, decision, report, memory) пишет trace.jsonl. Строгое правило as-of исключает утечку: ран, опубликованный позже момента выпуска, отклоняется. Решение publish/reissue принимают детерминированные правила или LLM (Pydantic AI + gpt-5.4-mini), причём ответ LLM проверяется кодом с откатом на правила.

Ключевые решения

  • LightGBM
  • MOS (линейная коррекция ветра)
  • логистическая кривая мощности
  • эмпирические квантили остатков P10/P90
  • as-of правило доступности ранов NWP
  • Pydantic AI
  • бэктест-replay
  • holdout-модель

Метрики

LLM-планировщик совпал с правилами в 58 из 58 решений за февраль, 0 откатов, $0.098 за месяц; сабмит 672 ч × 2 турбины

Особенности

Как у реальной диспетчерской: прогнозы погоды берутся только в том виде, в каком были опубликованы на момент выпуска, у каждой строки есть nwp_run_init_utc. Мощность считает модель, а LLM только принимает решение publish/reissue и пишет объяснение, и её ответ проверяется правилами. Replay февраля: 29 выпусков и 29 пересчётов с полным трейсом.

Стек

Python 3.12 · uv · pandas · pyarrow · LightGBM · Pydantic · Pydantic AI · Open-Meteo API · Vite · React 19 · Cloudflare Pages · Docker · Make · pytest

Модели

OpenAI gpt-5.4-mini

Проект создан за 5 часов на финале хакатона HackAlem.ai (23 сентября 2026, Астана). Код — BAITC-Hacks/hack-75766fed-localhosters.

README

README.md

WindAgent — прогноз выработки ВЭС «Нурлы» · Localhosters, HackAlem AI

Агент каждый день в 18:00 UTC выпускает почасовой прогноз мощности двух турбин на 48 часов в виде P10 / P50 / P90. Когда выходит новый ран погоды, агент пересчитывает прогноз и объясняет, что изменилось. Каждый шаг выпуска пишется в трейс, поэтому любой выпуск можно разобрать постфактум.

В репозитории — replay февраля 2026: 29 выпусков и 29 пересчётов. Каждый использует только те прогнозы погоды, которые были опубликованы к моменту выпуска.

Что нужноКоманда
"поставить зависимости"make setup
"проверить, что всё работает"make verify
"собрать сабмит февраля 2026"make backtest
"посчитать метрики на январе 2026"make backtest-dev
"посчитать метрики на феврале 2025"make backtest-feb2025
"запустить один выпуск агентом"uv run --frozen python -m windagent issue --at 2026-02-10T18:00Z --model-adapter windagent.model.v1:predict_power
"прогнать февраль с решениями LLM"make llm-replay
"открыть дашборд локально"make dashboard
"проверить в Docker"make docker-verify
"запустить юнит-тесты"make test
"переобучить LightGBM v1"make train

Основной сабмит: submission/forecast_test_hourly_v1.csv — 672 часа × 2 турбины = 1 344 строки. Дашборд: https://windagent-localhosters.pages.dev — логин localhosters, пароль [скрыто].

Описание решения и назначение

Задача кейса: почасовой прогноз выработки двух турбин на 24–48 часов. Весь цикл выполняет агент: погода → подготовка данных → модель → прогноз → анализ → пересчёт при обновлении входных данных. Условие кейса.

ВопросОтвет
Что прогнозируемнормализованную мощность 0–1 (доля от 2.5 МВт) каждой турбины и МВт по станции, по часам
Когда выпуск18:00 UTC накануне = 00:00 дня D по часам SCADA (UTC+6); горизонт 48 ч
Когда пересчёткогда появляется новый ран погоды: в 20:00 UTC становится доступен ECMWF 12Z
Откуда погодаOpen-Meteo Single Runs, ECMWF IFS 9 км — архив прогнозов в том виде, в каком они вышли
Что получает диспетчерP10 / P50 / P90 на 48 ч, решение publish / reissue с причиной, report.md на русском
Как проверить отсутствие утечкиу каждой строки есть nwp_run_init_utc; ран, опубликованный позже момента выпуска, отклоняется

Кому полезно: диспетчеру ВЭС для заявки на сутки вперёд и для решения, нужно ли обновлять заявку, когда пришла свежая погода.

Архитектура

┌─ офлайн: make train ─────────────────────────┐
│ SCADA 2 турбин: 10 мин → 1 ч, UTC+6          │
│ + архив прогнозов ECMWF IFS и Previous Runs  │
│ v0: MOS ветра + логистическая кривая         │
│ v1: LightGBM, p50 = 0.4·LightGBM + 0.6·v0    │
└───────────────────────┬──────────────────────┘
                        │ models/
                        ▼
┌─ каждый выпуск: run_issue(at) ───────────────┐
│ Clock: какой ран уже опубликован к at        │ ◄── Open-Meteo Single Runs
│ Проверка: 48 ч погоды без пропусков          │     ECMWF IFS 9 км, as-issued
│ Модель: P10 / P50 / P90, 48 ч × 2 турбины    │     data/nwp_cache/single_runs/
│ Сравнение с прошлым выпуском                 │
│ Планировщик: правила или OpenAI              │
│   → publish / reissue + объяснение           │
└───────────────────────┬──────────────────────┘
                        │ trace.jsonl · decision.json · report.md
                        ▼
       submission/forecast_test_*_v1.csv  ──►  дашборд

Модель считает мощность, LLM её не считает. Агент выбирает ран погоды по правилу as-of, проверяет входные данные, вызывает модель, сравнивает результат с прошлым выпуском и решает, публиковать ли выпуск и нужен ли пересчёт.

Шаг run_issueЧто делаетГде видно
read_memoryберёт прошлый выпуск для сравнения, только из прошлогоtrace.jsonl
get_clockфиксирует момент выпуска и задержки публикации рановtrace.jsonl
fetch_nwp_forecastберёт самый свежий ран, доступный к моменту выпускаinputs.json
fetch_scada_historyфиксирует границу as-of для SCADAtrace.jsonl
check_data_qualityпроверяет 48 ч погоды без пропусковdq_report.json
prepare_featuresвырезает окно горизонта из ранаinputs.json
predict_powerвызывает модель через общий контракт адаптера → P10 / P50 / P90forecast.csv
compare_with_previous + планировщикновый ран? сдвиг ветра? → publish / reissue / reject + объяснениеcomparison.json, decision.json
save_forecastсохраняет прогноз, если решено публиковатьforecast.csv
write_reportпишет отчёт для диспетчераreport.md
write_memoryсохраняет заметку для следующего выпускаmemory.json

Правило as-of. Ран ECMWF IFS доступен через 8 / 7 / 8 / 7 ч после старта циклов 00Z / 06Z / 12Z / 18Z (windagent/clock.py). В 18:00 UTC последний доступный ран — 06Z; 12Z становится доступен в 20:00 UTC, и агент делает пересчёт. Historical Forecast и ERA5 используются только для обучения: это не прогнозы в том виде, в каком они вышли.

Два режима планировщика. Режим scripted — детерминированные правила без сети и ключа, на нём построен сабмит. Режим openai — Pydantic AI + gpt-5.4-mini принимает то же решение и пишет объяснение. Код проверяет ответ LLM: ссылка на другой ран, коррекция, которой не было, или противоречие проверке качества → выпуск принимается по правилам, причина пишется в fallback_reason. На всём феврале LLM совпал с правилами в 58 решениях из 58, 0 откатов, $0.098 за месяц — отчёт.

Модели. v0 — линейный MOS ветра на 100 м и эмпирическая логистическая кривая мощности. v1 — LightGBM на признаках Single Runs и Previous Runs, P50 = 0.4 · LightGBM + 0.6 · v0. P10 / P90 — эмпирические квантили остатков v0 по корзинам мощности. На окнах с фактами (январь 2026, февраль 2025) работает holdout-модель models/lightgbm_v1_holdout.txt, которая этих месяцев не видела; для февраля 2026 — финальная models/lightgbm_v1.txt.

Подробнее: архитектура и ADR, правило as-of, допущения, LightGBM v1.

Используемые технологии

СлойТехнологияЗачем
ОкружениеPython 3.12, uv, uv.lockодна команда ставит зафиксированные версии
Данныеpandas, pyarrow (Parquet)SCADA 10 мин → 1 ч, архив прогнозов
ПогодаOpen-Meteo Single Runs и Previous Runs (ECMWF IFS), requests + requests-cacheпрогнозы с run_init_utc, офлайн-кеш в репозитории
МодельLightGBM 4.7, линейный MOS + логистическая кривая (v0)P10 / P50 / P90 на 48 ч
Агентсобственный runtime run_issue, контракты на Pydantic11 шагов, трейс, память, решения
LLMPydantic AI + OpenAI gpt-5.4-miniрешение publish / reissue и объяснение для диспетчера
ДашбордVite 7, React 19, Cloudflare Pages + Basic Authпрогноз, пересчёт и трейс каждого выпуска
Упаковка и проверкиMake, Docker, pytestвоспроизводимый запуск и проверка

Необходимые зависимости

ЧтоВерсияЗачемУстановка (macOS)
git, makeлюбыеклонирование и командыxcode-select --install
uv≥ 0.6ставит Python 3.12 и пакеты из uv.lockbrew install uv
libompлюбаяOpenMP для LightGBMbrew install libomp
Node.js20.19+ или 22.12+только дашбордbrew install node
Dockerлюбойтолько проверка в контейнереDocker Desktop
OPENAI_API_KEY—только режим LLMplatform.openai.com

CRITICAL (macOS): без brew install libomp make verify проходит первую половину и падает на второй с Library not loaded: @rpath/libomp.dylib. v0 работает без libomp, а LightGBM v1 — нет. На Debian/Ubuntu нужна библиотека libgomp1 (sudo apt-get install libgomp1); в Docker-образе она уже стоит.

Python-пакеты и их версии зафиксированы в pyproject.toml и uv.lock: requests, requests-cache, pandas, pyarrow, pydantic, pydantic-ai-slim[openai], python-dotenv, PyYAML, lightgbm; для тестов — pytest, responses, ruff. Версия Python задана в .python-version: uv сам скачает 3.12, даже если в системе стоит другая.

Сеть нужна только для установки пакетов. Прогнозы погоды, SCADA и обученные модели лежат в репозитории (data/ — 24 МБ, models/), поэтому бэктест и проверки идут офлайн.

Установка

git clone https://github.com/BAITC-Hacks/hack-75766fed-localhosters.git
cd hack-75766fed-localhosters
brew install uv libomp          # macOS; на Linux: uv + libgomp1
make setup                      # uv sync --frozen: Python 3.12 + пакеты из uv.lock
cp .env.example .env            # по желанию: ключ OpenAI для режима LLM

Через Docker (Python, uv и libgomp внутри образа):

docker build -t windagent .     # образ с кодом, архивом погоды и моделями
docker run --rm windagent       # запускает ту же проверку, что make verify

Параметры окружения

Шаблон — .env.example. Все переменные необязательны: без .env работают значения по умолчанию. .env читают python -m windagent и make llm-replay; make verify от него не зависит.

ПеременнаяПо умолчаниюЧто задаёт
OPENAI_API_KEYпустоключ для --llm openai и make llm-replay; режиму scripted не нужен
OPENAI_MODELgpt-5.4-miniмодель планировщика в режиме openai
LLM_MODEscriptedпланировщик по умолчанию для windagent issue
WINDAGENT_MODELv1модель сабмита для make backtest и make verify (v0 — эталон)
OPEN_METEO_CACHE_ONLY11 — только закоммиченный кеш погоды, без запросов в сеть
ISSUE_HOUR_UTC18час выпуска, UTC
HORIZON_HOURS48горизонт прогноза, ч
SCADA_TZ_OFFSET_HOURS6часовой пояс SCADA (фиксированный UTC+6)
REISSUE_ON_NEW_RUN11 — пересчёт на каждом новом ране; 0 — только при сдвиге ветра больше порога
DIVERGENCE_THRESHOLD_MS1.5порог среднего сдвига ветра на 100 м между ранами, м/с
POINT_ESTIMATEmedianточечный прогноз = P50
AVAIL_LAG_HOURS7задержка публикации для будущих не-ECMWF источников; для ECMWF задержки по циклам заданы в windagent/clock.py
RESIDUAL_Z_THRESHOLD, RESIDUAL_WINDOW_HOURS, DRIFT_WINDOW_DAYS2.5, 6, 14пороги триггеров по фактам; пока не исполняются (см. «Ограничения»)

Запуск

Сабмит февраля 2026 — 29 выпусков и 29 пересчётов через агента, ~5 секунд:

make backtest                   # = make backtest-v1: сабмит на LightGBM v1
make backtest-v0                # эталон v0 → submission/*_v0.csv, runs/backtest/v0/test/
ФайлСтрокЧто внутри
submission/forecast_test_hourly_v1.csv1 344основной сабмит: час × турбина, p50/p10/p90, power_mw; p50 из выпуска накануне, p50_h25_48 — из выпуска за двое суток
submission/forecast_test_dayahead_v1.csv2 78429 выпусков × 48 ч × 2 турбины, с nwp_run_init_utc и lead_h
submission/forecast_test_intraday_v1.csv2 784пересчёты в 20:00 UTC на ране 12Z, та же схема
submission/forecast_test_hourly_plant_v1.csv672станция целиком, МВт и МВт·ч по часам
runs/backtest/v1/test/58 папокпо папке на выпуск: trace.jsonl, decision.json, comparison.json, report.md, forecast.csv

Один выпуск агентом и пересчёт после выхода рана 12Z:

uv run --frozen python -m windagent issue --at 2026-02-10T18:00Z --model-adapter windagent.model.v1:predict_power               # day-ahead на ране 06Z
uv run --frozen python -m windagent issue --at 2026-02-10T20:00Z --model-adapter windagent.model.v1:predict_power               # пересчёт на ране 12Z
uv run --frozen python -m windagent issue --at 2026-02-10T18:00Z --model-adapter windagent.model.v1:predict_power --llm openai  # решение принимает LLM

Каждый запуск создаёт папку runs/<время>-<id>/ с forecast.csv, inputs.json, dq_report.json, comparison.json, decision.json, trace.jsonl, report.md, memory.json, status.json.

Частые ошибки (команда падает или считает не то):

WRONGRIGHT
python -m windagent issue --at 2026-02-10T18:00Z → MODEL_NOT_FOUNDдобавить --model-adapter windagent.model.v1:predict_power
uv run pytest при OPEN_METEO_CACHE_ONLY=1 в окружении → 5 тестов погоды падаютmake test (снимает флаг сам)
python -m windagent ... системным Pythonuv run --frozen python -m windagent ...

Метрики на окнах с фактами, LLM и дашборд:

make backtest-dev               # январь 2026 → reports/backtest_{v1,v0}_dev*.csv
make backtest-feb2025           # февраль 2025, тот же сезон → reports/backtest_{v1,v0}_feb2025*.csv
make llm-replay                 # февраль 2026 с решениями OpenAI: ~2.5 мин, ~$0.1, нужен OPENAI_API_KEY
make dashboard                  # экспорт данных + http://127.0.0.1:5173 (нужен Node.js)
make train                      # переобучить LightGBM v1 → models/lightgbm_v1*.txt

Развёрнутый дашборд: https://windagent-localhosters.pages.dev — логин localhosters, пароль [скрыто]. Сайт закрыт паролем, потому что в нём SCADA организаторов. На дашборде все 60 выпусков (январь с фактами и февраль), P10–P90, пересчёт 18:00 → 20:00 UTC, трейс агента по шагам и решения LLM рядом с правилами. Сценарий демо.

Проверка основного сценария

make verify                     # ~6 секунд, без сети и ключей

Ожидаемый вывод заканчивается двумя строками PASS:

PASS: 11 steps, 96 rows, 06Z→12Z revision, no future memory, rejected late/missing/demo inputs, TestModel
PASS: v1 — 29 issues via agent + 29 reissues (11-step traces); v0 and v1 agent == direct model; 2784 rows, 1344 hourly rows, all as-of, 19Z rejects 12Z, real 06Z→12Z revision
Что проверяетсяКак
Агент проходит все 11 шаговтрейс каждого из 58 выпусков содержит 11 строк
Пересчёт работает29 папок *-reissue на ране 12Z с reissue_recommended=true
Сабмит собран агентом, а не в обходпрогноз через run_issue совпадает с прямым расчётом модели, v0 и v1
Нет утечки будущегокаждая из 2 784 строк проходит assert_as_of; в 19:00 UTC ран 12Z отклоняется
Плохие входы отклоняютсяпоздний ран, пропущенный час, демо-данные в боевом режиме → ошибка
Контракт LLMответ проверяется схемой IssueDecision (Pydantic AI TestModel, без сети)
Объём сабмита2 784 строки по выпускам, 1 344 почасовых

Проверка перезаписывает submission/ и runs/backtest/ теми же байтами: после неё git status чистый. Если он не чистый — результат не воспроизвёлся.

Дополнительно:

make test                       # 69 юнит-тестов, ~9 секунд, включая tests/test_clock_no_leakage.py
make docker-verify              # docker build + та же проверка в контейнере

Проверка вручную на одном дне:

  1. Откройте runs/backtest/v1/test/2026-02-10T1800Z-dayahead/decision.json → "reason": "initial_issue", ран 2026-02-10T06:00:00Z.
  2. Откройте runs/backtest/v1/test/2026-02-10T2000Z-reissue/decision.json → "reason": "new_nwp_run", ран 2026-02-10T12:00:00Z, "reissue_recommended": true.
  3. В trace.jsonl той же папки 11 строк, по одной на шаг; в comparison.json — насколько сдвинулся прогноз.

Результаты

Фактов февраля 2026 в данных нет, поэтому качество измеряем тем же путём через агента на двух окнах с фактами, holdout-моделью:

ОкноМодельMAE T1MAE T2Медиана 7 сут. (T1 / T2)Покрытие P10–P90
январь 2026v10.1710.1750.294 / 0.2880.80
январь 2026v00.1740.1770.80
февраль 2025 (тот же сезон)v10.1650.1880.339 / 0.3040.80
февраль 2025 (тот же сезон)v00.1720.1920.79

MAE — по нормализованной мощности на всём горизонте 48 ч. Бейзлайн Previous Runs → кривая мощности даёт 0.20–0.22; цель ≤ 0.19 выполнена на обоих окнах. Блоки h1–24 / h25–48, режим без часов простоя и ограничений, skill: отчёт v1, отчёт v0, бейзлайны. Статус доказательств по критериям жюри — rubric-map.

Ограничения

  • Качество на феврале 2026 неизвестно: фактов нет. Оценка — январь 2026 и февраль 2025.
  • В момент выпуска модель не получает SCADA: шаг fetch_scada_history фиксирует только границу as-of. Прогноз строится по погоде; SCADA используется для обучения и оценки.
  • P10 / P90 — эмпирические квантили остатков v0, а не квантильная модель.
  • Пересчёты есть только в тестовом окне: в архиве января 2026 и февраля 2025 лежат только раны 06Z.
  • Триггеры по остаткам и дрейфу (RESIDUAL_*, DRIFT_*) описаны, но не исполняются: им нужны факты в реальном времени.
  • Задержки публикации ранов ECMWF (8 / 7 / 8 / 7 ч) — наша консервативная оценка, не официальная цифра Open-Meteo.
  • В выпуске один источник погоды — ECMWF IFS; другие модели Previous Runs входят только в признаки v1.
  • Дашборд — статический экспорт готовых выпусков, не live-сервис.

Структура репозитория

windagent/            агент: clock.py (as-of), agent/{runtime,planner,scripted,trace}.py,
                      model/{v0,v1,serve,schema}.py, backtest.py
scripts/              verify, verify_backtest, llm_replay, export_dashboard, flatten_submission
data/                 архив прогнозов ECMWF, Previous Runs, почасовая SCADA (24 МБ)
models/               v0_params.json, lightgbm_v1.txt, lightgbm_v1_holdout.txt
submission/           CSV сабмита (v1 — основной, v0 — эталон)
runs/backtest/        по папке на выпуск: trace, decision, report
runs/llm/test/        те же выпуски с решениями LLM
reports/              метрики и отчёт LLM
dashboard/            Vite/React, Cloudflare Pages
docs/                 архитектура, допущения, исследования
task context/         условие кейса и SCADA организаторов

Данные и атрибуция

Погода: Open-Meteo Single Runs и Previous Runs, модель ECMWF IFS. Запросы, сетка, время выгрузки и хеши — в data/nwp_cache/manifest.json, описание архива — в docs/03_WEATHER_ARCHIVE.md. Условие кейса и SCADA предоставлены организаторами HackAlem AI. Часовой пояс SCADA (UTC+6) проверен кросс-корреляцией с погодой: tz_xcorr.png, scripts/tz_check.py.

GitHub

BAITC-Hacks/hack-75766fed-localhosters
Звёзды
⭐ 0
Форки
0
Язык
Python
Последний push
в прошлом месяце
Коммиты
56
Авторы
4
Файлы
3000
Ветки
16

Авторы коммитов

birrrzha 31AkylbekOralov 17Kazybek Ramazan 7Kazybek Ramazan 1

История коммитов

  • 2026-09-23 17:54Merge pull request #15 from BAITC-Hacks/birrrzha/readme-dockerbirrrzha
  • 2026-09-23 17:53README to the organizers' spec, Docker fixed for v1, Python pinned to 3.12birrrzha
  • 2026-09-23 17:45feat: add calibrated LightGBM quantilesAkylbekOralov
  • 2026-09-23 17:42Merge pull request #13 from BAITC-Hacks/claude/loc-30-v1-backtestAkylbek Oralov
  • 2026-09-23 17:40LOC-30: feb2025 window through the agent, evaluate_forecast in both modes, v1 results in READMEAkylbekOralov
  • 2026-09-23 17:35Merge PR #12: submission on v1 through the agent, honest holdout serving, LLM guardrailbirrrzha
  • 2026-09-23 17:34Merge main (LOC-12 serving) into v1 switch: Akylbek's schema/serve path as the v1 adapter, holdout-free model for holdout-window issues, verify v1 agent == directbirrrzha
  • 2026-09-23 17:32Submission on LightGBM v1 through the agent; v0 kept as reference; LLM guardrail on the quality gatebirrrzha
Все коммиты (56)
  • 2026-09-23 17:25Merge pull request #11 from BAITC-Hacks/claude/loc-10-followup-07b8acAkylbek Oralov
  • 2026-09-23 17:23Merge origin/main (LOC-10 v1) and serve its model through the LOC-12 contractAkylbekOralov
  • 2026-09-23 17:17LOC-12: model contract — FEATURES, build_features(), predict(), metadata, v1 adapterAkylbekOralov
  • 2026-09-23 17:15Merge pull request #10 from BAITC-Hacks/codex/loc-10-lightgbm-v1Akylbek Oralov
  • 2026-09-23 17:12LOC-10: add lead-aligned LightGBM v1 spikeAkylbekOralov
  • 2026-09-23 17:08Merge: dashboard LLM vs rules tracebirrrzha
  • 2026-09-23 17:08Dashboard: LLM vs rules toggle in the agent trace, decision card with LLM explanation and tokensbirrrzha
  • 2026-09-23 17:07Merge PR #8: LLM planner live, February replay 58/58 (LOC-22)birrrzha
  • 2026-09-23 17:06Merge remote-tracking branch 'origin/main' into birrrzha/loc-22-llm-livebirrrzha
  • 2026-09-23 17:06LLM planner live on OpenAI gpt-5.4-mini: February replay 58/58 decisions agree with rules, fallback to rules (LOC-22)birrrzha
  • 2026-09-23 17:00Merge pull request #3 from BAITC-Hacks/codex/loc-15-previous-runsKazybek Ramazan
  • 2026-09-23 16:57Merge PR #7: backtest through the agent (LOC-33)birrrzha
  • 2026-09-23 16:57Backtest through the agent: every issue runs the full agent cycle, 12Z reissue at 20:00 UTC (LOC-33)birrrzha
  • 2026-09-23 16:56Merge latest main OpenAI integration into LOC-15Kazybek Ramazan
  • 2026-09-23 16:54Merge PR #6: OpenAI planner + team decisions on case questions (LOC-22, LOC-28)birrrzha
  • 2026-09-23 16:53Switch LLM planner to OpenAI; close open questions on the case with team decisions (LOC-22, LOC-28)birrrzha
  • 2026-09-23 16:53Merge latest main baseline changes into LOC-15Kazybek Ramazan
  • 2026-09-23 16:50Merge branch 'loc-9' into mainAkylbekOralov
  • 2026-09-23 16:49Merge main into LOC-15 Previous Runs branchKazybek Ramazan
  • 2026-09-23 16:39Deploy dashboard to Cloudflare Pages behind password (Basic Auth middleware), README linkbirrrzha
  • 2026-09-23 16:34Merge PR #5: interactive dashboard (LOC-34, LOC-35)birrrzha
  • 2026-09-23 16:33Interactive dashboard: all 60 issues, P10–P90, hover readout, revision scene, agent trace (LOC-34, LOC-35)birrrzha
  • 2026-09-23 16:33Fix agent runtime on large NWP cache: pick run by file name, parse newest first, skip runs with missing valuesbirrrzha
  • 2026-09-23 16:28LOC-9: metrics module and honest baselines / ceilings tableAkylbekOralov
  • 2026-09-23 16:27LOC-18: commit Single Runs response cache for offline rebuildAkylbekOralov
  • 2026-09-23 16:27Merge branch 'loc-18': Single Runs ecmwf_ifs train dump (LOC-18)AkylbekOralov
  • 2026-09-23 16:18LOC-18: Single Runs ecmwf_ifs train dump 2024-03-14..2026-01-31 (go)AkylbekOralov
  • 2026-09-23 16:16Add agent issue runner and dashboardbirrrzha
  • 2026-09-23 16:14Align integration with merged backtestbirrrzha
  • 2026-09-23 16:12Merge remote-tracking branch 'origin/main' into birrrzha/agent-foundationbirrrzha
  • 2026-09-23 16:11Merge PR #2: Backtest v0 — as-of clock, MOS+curve model, Feb 2026 submission (LOC-16, LOC-17)birrrzha
  • 2026-09-23 16:11Merge backtest-v0 into agent-foundationbirrrzha
  • 2026-09-23 16:07Merge origin/main into agent-foundationbirrrzha
  • 2026-09-23 16:03Add as-of backtest, agent trace and dashboardbirrrzha
  • 2026-09-23 15:58Merge branch 'main' of github-AkylbekOralov:BAITC-Hacks/hack-75766fed-localhostersAkylbekOralov
  • 2026-09-23 15:57Research docs: apply verification corrections (per-cycle run availability, honest baselines vs ceilings, free-tier limits, archive starts), MVP scope, final plan with Linear IDs, 7th questionbirrrzha
  • 2026-09-23 15:57Complete LOC-8 SCADA hourly datasetAkylbekOralov
  • 2026-09-23 15:56Build LOC-15 Previous Runs archive for five modelsKazybek Ramazan
  • 2026-09-23 15:52Normalized NwpForecast cache (147 runs) for the agent runtime, built from LOC-14 parquet with per-cycle availabilitybirrrzha
  • 2026-09-23 15:51Backtest v0: as-of clock with per-cycle availability, MOS+curve model, Feb 2026 dayahead replay + hourly submission, leak tests (LOC-16, LOC-17)birrrzha
  • 2026-09-23 15:41Merge pull request #1 from BAITC-Hacks/codex/loc-14-weather-archiveKazybek Ramazan
  • 2026-09-23 15:41Merge branch 'main' into codex/loc-14-weather-archiveKazybek Ramazan
  • 2026-09-23 15:39Implement LOC-14 cached Open-Meteo archive and offline validationKazybek Ramazan
  • 2026-09-23 15:34added remote gpu skillAkylbekOralov
  • 2026-09-23 15:10Research handoff: verified findings, plan with Linear links, Open-Meteo client + SCADA loader + tz check, cached NWP archives for Feb 2026birrrzha
  • 2026-09-23 14:51Research: questions for organizers with default assumptions (LOC-28)birrrzha
  • 2026-09-23 14:35added ml integration explanationsAkylbekOralov
  • 2026-09-23 13:45Start research phase: case summary, team roles, research plan, datasetbirrrzha
Структура репозитория (3000 файлов)
.claude/skills/gpu-remote/SKILL.md
.dockerignore
.env.example
.gitignore
.python-version
Dockerfile
Makefile
README.md
config/sites/T1.yaml
config/sites/T2.yaml
dashboard/functions/_middleware.js
dashboard/index.html
dashboard/package-lock.json
dashboard/package.json
dashboard/public/data/agent.json
dashboard/public/data/dev.json
dashboard/public/data/forecast_test_hourly_v1.csv
dashboard/public/data/index.json
dashboard/public/data/test.json
dashboard/public/favicon.svg
dashboard/src/main.jsx
dashboard/src/style.css
data/baselines/README.md
data/baselines/era5_ws100.parquet
data/baselines/previous_runs_ws100.parquet
data/era5/era5_2023-07.csv
data/era5/era5_2023-12.csv
data/era5/era5_2024-02.csv
data/era5/era5_2024-04.csv
data/era5/era5_2025-01.csv
data/era5/era5_2025-02.csv
data/era5/era5_2025-07.csv
data/era5/era5_2025-10.csv
data/era5/era5_2025-11.csv
data/era5/era5_2025-12.csv
data/era5/era5_2026-01.csv
data/nwp/single_runs_ecmwf_ifs_feb2026.parquet
data/nwp/single_runs_ecmwf_ifs_jan2026.parquet
data/nwp/single_runs_ecmwf_ifs_train.manifest.json
data/nwp/single_runs_ecmwf_ifs_train.parquet
data/nwp_cache/manifest.json
data/nwp_cache/normalized/ecmwf_ifs_2025-12-31T06Z.json
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runs/backtest/v0/test/2026-02-04T2000Z-reissue/inputs.json
runs/backtest/v0/test/2026-02-04T2000Z-reissue/memory.json
runs/backtest/v0/test/2026-02-04T2000Z-reissue/report.md
runs/backtest/v0/test/2026-02-04T2000Z-reissue/status.json
runs/backtest/v0/test/2026-02-04T2000Z-reissue/trace.jsonl
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/comparison.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/decision.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/dq_report.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/forecast.csv
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/inputs.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/memory.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/report.md
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/status.json
runs/backtest/v0/test/2026-02-05T1800Z-dayahead/trace.jsonl
runs/backtest/v0/test/2026-02-05T2000Z-reissue/comparison.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/decision.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/dq_report.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/forecast.csv
runs/backtest/v0/test/2026-02-05T2000Z-reissue/inputs.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/memory.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/report.md
runs/backtest/v0/test/2026-02-05T2000Z-reissue/status.json
runs/backtest/v0/test/2026-02-05T2000Z-reissue/trace.jsonl
runs/backtest/v0/test/2026-02-06T1800Z-dayahead/comparison.json
runs/backtest/v0/test/2026-02-06T1800Z-dayahead/decision.json
runs/backtest/v0/test/2026-02-06T1800Z-dayahead/dq_report.json
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runs/backtest/v0/test/2026-02-06T1800Z-dayahead/inputs.json
runs/backtest/v0/test/2026-02-06T1800Z-dayahead/memory.json
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runs/backtest/v0/test/2026-02-06T1800Z-dayahead/status.json
runs/backtest/v0/test/2026-02-06T1800Z-dayahead/trace.jsonl
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runs/backtest/v0/test/2026-02-06T2000Z-reissue/decision.json
runs/backtest/v0/test/2026-02-06T2000Z-reissue/dq_report.json
runs/backtest/v0/test/2026-02-06T2000Z-reissue/forecast.csv
runs/backtest/v0/test/2026-02-06T2000Z-reissue/inputs.json
runs/backtest/v0/test/2026-02-06T2000Z-reissue/memory.json
runs/backtest/v0/test/2026-02-06T2000Z-reissue/report.md
runs/backtest/v0/test/2026-02-06T2000Z-reissue/status.json
runs/backtest/v0/test/2026-02-06T2000Z-reissue/trace.jsonl
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/comparison.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/decision.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/dq_report.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/forecast.csv
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/inputs.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/memory.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/report.md
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/status.json
runs/backtest/v0/test/2026-02-07T1800Z-dayahead/trace.jsonl
runs/backtest/v0/test/2026-02-07T2000Z-reissue/comparison.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/decision.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/dq_report.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/forecast.csv
runs/backtest/v0/test/2026-02-07T2000Z-reissue/inputs.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/memory.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/report.md
runs/backtest/v0/test/2026-02-07T2000Z-reissue/status.json
runs/backtest/v0/test/2026-02-07T2000Z-reissue/trace.jsonl
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/comparison.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/decision.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/dq_report.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/forecast.csv
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/inputs.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/memory.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/report.md
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/status.json
runs/backtest/v0/test/2026-02-08T1800Z-dayahead/trace.jsonl
runs/backtest/v0/test/2026-02-08T2000Z-reissue/comparison.json
runs/backtest/v0/test/2026-02-08T2000Z-reissue/decision.json
Ветки (16)
birrrzha/agent-foundationbirrrzha/backtest-v0birrrzha/dashboard-llm-tracebirrrzha/loc-22-llm-livebirrrzha/loc-22-openai-plannerbirrrzha/loc-33-backtest-via-agentbirrrzha/loc-34-35-dashboardbirrrzha/readme-dockerbirrrzha/v1-in-agentclaude/loc-10-followup-07b8acclaude/loc-30-v1-backtestcodex/loc-10-lightgbm-v1codex/loc-11-quantilescodex/loc-14-weather-archivecodex/loc-15-previous-runsmain

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