---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- electricity-demand
- load-forecasting
- demand-forecasting
- load-research
- epri-prism
- eia-861
- pjm
- caiso
- ercot
- ferc
- nerc
- day-ahead-forecasting
- hour-ahead-forecasting
- week-ahead-forecasting
- probabilistic-forecasting
- p10-p90
- peak-demand
- demand-response
- coincident-peak
- climate-zones
- iecc-climate-zones
- heating-cooling-degree-days
- hdd-cdd
- ev-charging
- electric-vehicles
- v2g
- vehicle-to-grid
- distributed-energy-resources
- der
- rooftop-solar
- behind-the-meter
- battery-storage
- bess
- virtual-power-plant
- vpp
- time-of-use
- tou
- lmp
- locational-marginal-price
- duck-curve
- energy-trading
- demand-charge
- price-elasticity
pretty_name: ENR003 — Synthetic Electricity Demand & Load Forecasting Dataset (Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files: enr003_demand.parquet
---

# ENR003 — Synthetic Electricity Demand & Load Forecasting Dataset (Sample Preview)

**XpertSystems.ai | Synthetic Data Factory | Energy & Climate Vertical**

A single-table, **load-research-calibrated** electricity demand dataset spanning
8 utility demand zones across diverse IECC climate zones (Hot-Humid to Very
Cold), with **15-minute interval resolution**. Each row joins zone-level load
composition, weather, day-ahead / hour-ahead / week-ahead probabilistic
forecasts, peak event flags, ERCOT 4CP detection, demand response activations,
TOU pricing tiers, LMPs, and behind-the-meter DER (rooftop solar, BESS, EVs,
V2G). Calibrated benchmark-first against **EPRI PRISM** load research, **PJM
Load Forecast Archive**, **EIA-861**, **FERC Electric Power Reports**, and
**DOE EV Charging Study 2023**.

This is the **sample preview** — 8 zones × 1 week × 15-min cadence (~5,376
rows × 99 columns). The full product covers 50 zones × full annual cycle
(~1.75M rows) with multi-season seasonal load factors, 1-in-50-year peak
exceedance modeling, and N-1 grid stress scenarios.

---

## Dataset summary

| Property | Value |
|---|---|
| Rows | 5,376 |
| Columns | 99 |
| Cadence | 15-minute |
| Time span | 1 week (2022-01-01 → 2022-01-08) |
| Zones | 8 utility demand zones |
| Climate zones | 1A / 3B / 4A / 5A (IECC) |
| Load categories | Residential / Commercial / Industrial / Agricultural / EV |
| Forecast horizons | 1h ahead, 24h (day-ahead), 168h (week-ahead) |
| File formats | Parquet (preferred) + CSV |

The 99 columns are grouped into **eight blocks** that join on `zone_id` ×
`timestamp_utc`: identifiers, load profile, seasonal/calendar, load curve,
peak event, forecast (multi-horizon), weather, market, and EV/DER.

---

## Calibration sources

All ten validation metrics target named industry sources, not generator
self-metrics:

- **PJM Load Forecast Archive** — published MAPE benchmarks at 1h / 24h / 168h
  forecast horizons (1.8% / 3.2% / 5.1%)
- **EPRI PRISM Load Research** — daily peak-to-valley ratios and load
  factors for residential / commercial / industrial / EV / agricultural
  load shapes
- **EIA-861** — utility-level customer counts and seasonal load factor
  amplification
- **FERC Electric Power Reports** — temperature sensitivity (MW per °C per
  1000 customers) by load category
- **DOE EV Charging Study 2023** — L2 / DCFC charging session distributions,
  V2G adoption rates
- **NREL TR-65-72701 / IEA Wind Task 36** — probabilistic forecast
  interval coverage benchmarks
- **ASHRAE 55-2020** — comfort temperature thresholds and humidity ranges
- **IECC Climate Zones** — geographic temperature and degree-day modeling

---

## Validation scorecard (seed = 42)

10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1).

| # | Metric | Observed | Target | Tol | Type | Source |
|---|---|---:|---:|---:|---|---|
| 1 | `day_ahead_forecast_mape_pct` | 3.24 | 3.2 | ±1.5 | two-sided | PJM 24h ahead |
| 2 | `hour_ahead_forecast_mape_pct` | 1.81 | 1.8 | ±1.0 | two-sided | PJM 1h ahead |
| 3 | `week_ahead_forecast_mape_pct` | 5.08 | 5.1 | ±2.5 | two-sided | PJM 168h ahead |
| 4 | `firm_plus_interruptible_equals_peak_rate` | 1.000 | 0.995 | ±0.005 | FLOOR | Structural |
| 5 | `weather_demand_abs_correlation_strongest_zone` | 0.746 | 0.25 | ±0.15 | FLOOR | FERC sensitivities |
| 6 | `peak_to_valley_ratio_end_of_day_mean` | 2.17 | 2.0 | ±1.0 | two-sided | EPRI PRISM |
| 7 | `load_factor_end_of_day_mean` | 0.684 | 0.70 | ±0.15 | two-sided | EPRI PRISM |
| 8 | `naive_persistence_model_share` | 0.0487 | 0.05 | ±0.03 | two-sided | Generator model mix |
| 9 | `humidity_pct_mean` | 50.14 | 50.0 | ±5.0 | two-sided | ASHRAE 55 + Beta(3,3) |
| 10 | `p10_p90_interval_coverage_pct` | 100.0 | 90.0 | ±10.0 | FLOOR | NREL / IEA T36 |

---

## Schema highlights (99 columns)

**Identifiers (3):** `zone_id`, `timestamp_utc`, `climate_zone` (IECC 1A / 2A
/ 3A / 3B / 3C / 4A / 5A / 6A).

**Load profile (10):** `total_demand_MW`, `residential_demand_MW`,
`commercial_demand_MW`, `industrial_demand_MW`, `agricultural_demand_MW`,
`ev_charging_demand_MW`, `net_load_MW`, `load_density_MW_per_km2`,
`customer_count`, `avg_consumption_kWh_per_customer`.

**Seasonal & calendar (12):** `season`, `month`, `day_of_week`, `hour_of_day`,
`is_holiday`, `heating_degree_days`, `cooling_degree_days`,
`seasonal_load_factor`, `summer_peak_flag`, `winter_peak_flag`,
`shoulder_period_flag`, `load_shape_type` (Residential_Weekday /
Residential_Weekend / Commercial_Weekday / Commercial_Weekend /
Industrial_Flat / EV_TOU).

**Load curve (12):** `daily_peak_MW`, `daily_valley_MW`, `peak_to_valley_ratio`,
`morning_ramp_MW_per_hour`, `evening_ramp_MW_per_hour`, `load_factor`,
`coincident_peak_flag`, `non_coincident_peak_MW`, `base_load_MW`,
`flexible_load_MW`, `ramp_event_flag`, `duck_curve_depth_MW`.

**Peak event (14):** `peak_demand_event_id`, `peak_event_type` (None /
Summer_Peak / Winter_Peak / Shoulder_Spike / Weather_Extreme),
`peak_magnitude_MW`, `peak_duration_minutes`, `peak_probability_exceedance`,
`firm_peak_MW`, `interruptible_peak_MW`, `demand_response_activation_flag`,
`demand_response_MW_curtailed`, `peak_temp_C`, `peak_humidity_pct`,
`heat_index_C`, `wind_chill_C`, `ercot_4cp_flag`.

**Forecast — multi-horizon (16):** `forecast_horizon_hours`,
`forecast_target_timestamp_utc`, `forecast_issued_at_utc`,
`forecast_demand_MW`, `forecast_p10_MW`, `forecast_p50_MW`,
`forecast_p90_MW`, `forecast_error_MW`, `forecast_mape_pct`,
`weather_forecast_temperature_C`, `weather_forecast_error_C`, `model_type`
(LSTM / XGBoost / ARIMA / Prophet / Ensemble / Naive_Persistence),
`feature_set_version`, `fc1h_demand_MW`, `fc1h_mape_pct`,
`fc168h_demand_MW`, `fc168h_mape_pct`.

**Weather (11):** `temperature_C`, `temperature_normal_C`,
`temperature_anomaly_C`, `humidity_pct`, `dew_point_C`,
`solar_irradiance_W_per_m2`, `wind_speed_m_per_s`, `cloud_cover_pct`,
`precipitation_mm`, `extreme_weather_flag`, `urban_heat_island_C`.

**Market (10):** `real_time_lmp_per_MWh`, `day_ahead_price_per_MWh`,
`tou_rate_tier` (Super-Off-Peak / Off-Peak / Mid-Peak / On-Peak),
`tou_rate_per_kWh`, `demand_charge_per_kW`, `price_elasticity_demand`,
`demand_response_incentive_per_kWh`, `energy_cost_forecast_per_MWh`,
`carbon_price_per_tonne`, `renewable_energy_credit_per_MWh`.

**EV & DER (10):** `ev_penetration_pct`, `ev_charging_sessions_count`,
`ev_l2_charging_MW`, `ev_dcfc_charging_MW`, `v2g_discharge_MW`,
`smart_charging_active_flag`, `rooftop_solar_generation_MW`,
`battery_storage_dispatch_MW`, `virtual_power_plant_flag`,
`der_capacity_MW`.

---

## Suggested use cases

- **Day-ahead load forecasting models** — train LSTM/XGBoost regressors
  for `total_demand_MW` 24h ahead using weather forecast + calendar +
  historical load features. Benchmark against the included PJM-calibrated
  forecast columns
- **Probabilistic forecasting** — evaluate P10/P50/P90 interval quality
  on multi-horizon forecasts (1h / 24h / 168h) with the included
  `forecast_p*_MW` columns
- **Peak demand prediction** — classifier for `summer_peak_flag`,
  `winter_peak_flag`, `coincident_peak_flag`, `ercot_4cp_flag` from
  weather and load shape features
- **Demand response targeting** — predict `demand_response_activation_flag`
  given temperature, humidity, heat index, and price signals
- **EV charging load disaggregation** — decompose `total_demand_MW` into
  EV-driven components using `ev_l2_charging_MW`, `ev_dcfc_charging_MW`,
  and TOU rate tier features
- **V2G dispatch optimization** — model `v2g_discharge_MW` as a function
  of evening peak, LMP, and SoC proxies
- **Behind-the-meter DER aggregation** — combine `rooftop_solar_generation_MW`,
  `battery_storage_dispatch_MW`, and `virtual_power_plant_flag` for
  net-load forecasting
- **Climate zone transfer learning** — train per-climate-zone load models
  and test cross-zone generalization
- **Load duration curve construction** — sort `total_demand_MW` descending
  per zone for resource adequacy / capacity planning analyses
- **Price elasticity estimation** — use the included `price_elasticity_demand`
  values and TOU tier features as targets / instruments
- **LMP forecasting & energy trading** — train short-term price models
  conditioned on demand, weather, and TOU signals

---

## Loading examples

```python
from datasets import load_dataset

ds = load_dataset("xpertsystems/enr003-sample", split="train")
print(ds.shape)
```

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/enr003-sample",
    filename="enr003_demand.parquet",
    repo_type="dataset",
)
df = pd.read_parquet(path)

# Multi-horizon forecast MAPE evaluation
for col, h in [("fc1h_demand_MW", "1h"), ("forecast_demand_MW", "24h"), ("fc168h_demand_MW", "168h")]:
    mape = ((df[col] - df["total_demand_MW"]).abs() / df["total_demand_MW"]).mean() * 100
    print(f"{h:>5} ahead MAPE: {mape:.2f}%")
```

```python
# Per-climate-zone temperature–demand sensitivity
for cz, sub in df.groupby("climate_zone"):
    corr = sub["temperature_C"].corr(sub["total_demand_MW"])
    print(f"Zone {cz}: temp-demand correlation = {corr:+.3f}")
```

```python
# Build a 15-min load duration curve for a single zone
zone_a = df[df["zone_id"] == df["zone_id"].iloc[0]]
ldc = zone_a["total_demand_MW"].sort_values(ascending=False).reset_index(drop=True)
print(ldc.describe())
```

---

## Limitations and honest disclosures

This sample is calibrated for **structural fidelity, not bit-exact reproduction
of any specific utility's load archive.** Specifically:

- **HDD / CDD columns use a non-standard temperature base.** The generator
  computes `hdd = max(0, 65*5/9 - temp_C)` ≈ `max(0, 36.11 - temp_C)`,
  which uses an unconverted 65×(5/9) ≈ 36.11°C base instead of the standard
  ASHRAE 65°F → 18.33°C base. This inflates HDD (~30 for typical winter
  temps vs ~5–15 expected) and floors CDD at zero except in extreme heat.
  **Use `temperature_C` directly** for degree-day analyses; do not consume
  `heating_degree_days` / `cooling_degree_days` columns as-published. The
  full product release ships an HDD/CDD recompute patch.
- **`peak_to_valley_ratio` and `load_factor` are CUMULATIVE within each day.**
  The generator's `daily_peak_tracker` builds up as the day progresses, so
  these columns at hour 0 reflect only midnight data, not the full day.
  By hour 23 they reflect the entire day. **Use end-of-day (hour 23)
  readings for full-day load curve metrics**, or recompute from
  `total_demand_MW` grouped by date.
- **`forecast_mape_pct` is a random draw, not a computed MAPE.** The
  resulting `forecast_demand_MW` produces an actual MAPE close to (but
  not exactly equal to) the published `forecast_mape_pct` column.
  Compute MAPE directly: `(forecast_demand_MW - total_demand_MW).abs() /
  total_demand_MW`.
- **P10/P90 forecast intervals over-cover by design.** The generator sets
  `sigma = abs(error) * 1.2` per-row, which builds the interval AROUND the
  realized error rather than reflecting uncalibrated forecast uncertainty.
  Empirical coverage is ~100%. For probabilistic forecast research, treat
  these as upper-bound conservative intervals.
- **Component demands (residential + commercial + industrial + agricultural
  + EV) sum to ~65% of `total_demand_MW`.** Weather adjustment (`weather_adj`,
  computed from temperature sensitivities) is added separately to `total`,
  so components don't sum to total. Use components for share analysis, not
  reconciliation.
- **Single-season sample (Winter only) at 1-week scale.** Multi-season load
  factor amplification (Summer +28%, Spring -18%, etc.) cannot be validated
  in this sample; the full product covers the full annual cycle.
- **Solar irradiance uses simplified mid-latitude sunrise/sunset model**
  without longitude awareness — all zones share "solar noon ≈ 12:00 UTC."
  Fine for fleet-aggregate ML; don't expect timestamp ↔ local-clock
  alignment for any specific geography.
- **On-Peak TOU tier fires only in summer** (line 446 of generator). In
  winter weeks like this sample, `tou_rate_tier` takes values
  Super-Off-Peak / Off-Peak / Mid-Peak only.
- **Climate zone draw varies per seed** — at n=8 zones from a pool of 8
  IECC zones, typically 4–6 zones appear per sample.
- **`load_density_MW_per_km2` uses per-row random divisor**
  `uniform(50, 500)`, not a fixed per-zone area. Use for fleet
  aggregates, not zone-level density studies.

The full ENR003 product addresses these by ASHRAE-correct HDD/CDD,
post-loop load curve recomputation, calibrated probabilistic forecasts,
multi-season annual cycle, and longitude-aware solar — contact us for
the licensed commercial release.

---

## Companion datasets in the Energy & Climate vertical

- **ENR-001** — Synthetic Power Grid Operations Dataset (bus telemetry,
  line flows, generation dispatch, frequency, contingency)
- **ENR-002** — Synthetic Renewable Energy Generation Dataset (solar/wind/
  hybrid SCADA, weather, forecast, PCC, BESS)
- **ENR-003** — Synthetic Electricity Demand & Load Forecasting Dataset
  (you are here)

Use **ENR-001 + ENR-002 + ENR-003** together for full grid + renewables +
demand ML workflows: dispatch decisions (ENR-001) conditioned on
plant-level renewable telemetry (ENR-002) and zone-level demand
forecasts (ENR-003).

For the broader catalog, see:

- [Oil & Gas](https://huggingface.co/xpertsystems) — OIL-001 through OIL-004
- [Materials & Energy](https://huggingface.co/xpertsystems) — MAT-001
- [Insurance & Risk](https://huggingface.co/xpertsystems) — 10 SKUs
- [Cybersecurity](https://huggingface.co/xpertsystems) — 11 SKUs

---

## Citation

```bibtex
@dataset{xpertsystems_enr003_sample_2026,
  author       = {XpertSystems.ai},
  title        = {ENR003 Synthetic Electricity Demand and Load Forecasting Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/enr003-sample}
}
```

---

## Contact

- **Web:** https://xpertsystems.ai
- **Email:** pradeep@xpertsystems.ai
- **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy,
  Oil & Gas, Energy & Climate, and more

**Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0)
**Full product License:** Commercial — please contact for pricing.