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README.md

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+---
+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.

enr003_demand.parquet

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