happyman11/Delhi-SLDC

Delhi Grid Load & Weather (Apr–Aug 2024)

Two time-aligned CSV files covering the Delhi electricity grid's operational state and local weather, sampled every few minutes over a 115-day span in 2024 (55 of which have data — see Coverage and gaps below):

• Delhi.csv — grid/power-system readings: instantaneous load, scheduled load, drawal, over/under-drawal, in-state generation, and grid frequency, plus same-day and previous-day operational summaries.
• Weather_Delhi.csv — co-located weather observations: temperature, humidity, wind, cloud cover, and a categorical weather description.

Both files share a TIME STAMP column and are meant to be inner-joined on it. This dataset backs a physics-informed multi-horizon load/frequency forecasting project; the raw files here are exactly as collected, with no resampling, imputation, or feature engineering applied.

Dataset structure

File	Rows	Columns	Time range
Delhi.csv	7,751	17	2024-04-22 10:07:28 → 2024-08-14 19:55:42
Weather_Delhi.csv	7,750	14	2024-04-22 10:07:28 → 2024-08-14 19:55:42

Joining on TIME STAMP (inner join) yields 7,750 aligned rows (1 timestamp in Delhi.csv has no weather match).

Delhi.csv fields

Column	Type	Description
TIME STAMP	datetime string	Join key; YYYY-MM-DD HH:MM:SS
currentfrequency	float	Grid frequency, Hz (observed range 49.59–50.38; nominal 50 Hz)
dsm_rate	int	Demand Side Management rate/regime code; only 2 values observed in this file (0, 401)
load	int	Total instantaneous grid load, MW (observed range 3,725–8,636)
scheduled_load	int	Scheduled/contracted load, MW
load_drawal	int	Power actually drawn from the grid, MW
od_ud	int	Over/under-drawal, MW — signed (load_drawal - scheduled_load); can be negative
generation_load	int	Local/in-state generation component, MW
max_load_today, min_load_today	int	Running max/min load for the current day as of this reading (operational, as-of statistic — not a fixed daily value)
max_load_today_time, min_load_today_time	string	Time-of-day (HH:MM:SS) those extrema occurred
max_load_yesterday, min_load_yesterday	int	Previous day's max/min load
max_load_yesterday_time, min_load_yesterday_time	string	Time-of-day those extrema occurred
filled_at	string	HH:MM ingestion/logging marker; not a physical measurement

load_drawal = scheduled_load + od_ud holds exactly in this file (verified to a residual of 0.0 across all 7,751 rows). load ≈ load_drawal + generation_load holds approximately (mean residual 0.04 MW, std 4.56 MW, 97% of rows within ±10 MW, max absolute deviation 174 MW).

Weather_Delhi.csv fields

Column	Type	Description
TIME STAMP	datetime string	Join key, same format as Delhi.csv
weather_description	string	Categorical condition (17 unique values observed: clear sky, haze, few clouds, scattered clouds, broken clouds, overcast clouds, mist, dust, drizzle, light intensity drizzle, light rain, moderate rain, heavy intensity rain, very heavy rain, thunderstorm, thunderstorm with light rain, thunderstorm with rain)
weather_temp	float	Temperature, °C (observed range 24.96–45.05; inferred from plausible range for Delhi Apr–Aug, not explicitly labeled in source)
weather_feels_like	float	Apparent temperature, °C (observed range 25.96–49.96)
weather_temp_min, weather_temp_max	float	Local min/max temperature at observation time; near-duplicate of weather_temp at most timestamps
weather_temp_pressure	int	Atmospheric pressure, hPa
weather_temp_humidity	int	Relative humidity, % (0–100)
weather_temp_visibility	int	Visibility, meters
weather_temp_sunrise, weather_temp_sunset	int	Sunrise/sunset time, Unix epoch seconds
weather_wind_speed	float	Wind speed, m/s (observed range 0–6.69)
weather_wind_deg	int	Wind direction, degrees
weather_clouds_all	int	Cloud cover, % (0–100)

Column naming and value ranges are consistent with the OpenWeatherMap Current Weather API schema; this is an inference from the data's shape, not a confirmed attribution — verify before relying on it.

Coverage and gaps

• Native sampling is irregular: median interval 11.1 minutes, with a 10th–90th percentile band of 1.3–11.2 minutes (i.e., frequent sub-minute bursts mixed with the ~11-minute baseline).
• Only 55 of the 115 calendar days in the nominal date range actually have data. The largest single gap is approximately 862 hours (~36 days). Do not treat this as a continuous time series — segment first on any gap larger than your tolerance before windowing or interpolating.
• -9999 is used elsewhere in this data family as a missing-value sentinel (in both numeric and string form), but no -9999 values are present in either file as currently exported — still worth checking for defensively in any downstream pipeline, since the exporter that produced these files may emit it under different conditions.
• max_load_today / min_load_today (and their *_yesterday counterparts) are as-of operational summaries computed by the source system at read time, not fixed daily aggregates — don't use them as a leakage-free daily max/min without checking what portion of the day had elapsed at each timestamp.

Usage

Load either config with the datasets library (each is a single train split, since the source is one CSV per config):

from datasets import load_dataset

power = load_dataset("happyman11/Delhi-SLDC", "power", split="train")
weather = load_dataset("happyman11/Delhi-SLDC", "weather", split="train")

print(power[0])
print(power.features)

To reproduce the inner join on TIME STAMP described above, purely with Dataset.map/Dataset.filter (no pandas):

weather_by_time = {row["TIME STAMP"]: row for row in weather}
weather_cols = [c for c in weather.column_names if c != "TIME STAMP"]

def attach_weather(example):
    match = weather_by_time.get(example["TIME STAMP"])
    extra = {c: match[c] for c in weather_cols} if match else {c: None for c in weather_cols}
    return {**example, **extra, "_matched": match is not None}

# every row needs the same schema for Dataset.map -- unmatched rows get None
# in the weather columns rather than omitting the keys, or map() raises a
# schema-mismatch error partway through the batch that happens to contain
# the one power-only timestamp with no weather match.
joined = power.map(attach_weather).filter(lambda ex: ex["_matched"]).remove_columns("_matched")
print(len(joined))  # 7,750

Dataset creation

Source (inferred, not independently confirmed): the power/grid file's column set (currentfrequency, load, scheduled_load, load_drawal, od_ud, generation_load, dsm_rate) matches the real-time data published by the Delhi State Load Despatch Centre (SLDC); the weather file's schema matches a standard current-weather API response for Delhi. Neither source is confirmed by metadata in the files themselves — treat this section as a best-effort inference, not a citation.

Collection process: unknown beyond what's inferable from the data (apparent periodic polling of the two sources, joined only by shared timestamp, no documented collection code included in this repository).

Considerations for using this data

• License / redistribution rights are not established. This dataset card is shipped with license: unknown deliberately. If the power data originates from Delhi SLDC and the weather data from a commercial weather API, both sources likely have their own terms of use governing redistribution. Confirm you have the right to redistribute this data before making this repository public or using it beyond personal / research purposes.
• No personally identifiable information is present — this is aggregate grid telemetry and weather data.
• Only 55 observed days, all within April–August 2024: this supports short-term, same-season forecasting research on the observed period only, not claims about seasonal, annual, or year-over-year patterns.

Licensing information

Not specified. See "Considerations for using this data" above.

Citation

No canonical citation is available for this raw export. If you use this dataset, please describe its provenance (Delhi SLDC + weather API, as inferred above) and link back to wherever you obtained it.