Datasets:
Formats:
parquet
Size:
10K - 100K
Tags:
synthetic-data
wholesale-energy-market
electricity-trading
lmp
locational-marginal-price
day-ahead-market
License:
| license: cc-by-nc-4.0 | |
| task_categories: | |
| - tabular-classification | |
| - tabular-regression | |
| - time-series-forecasting | |
| tags: | |
| - synthetic-data | |
| - wholesale-energy-market | |
| - electricity-trading | |
| - lmp | |
| - locational-marginal-price | |
| - day-ahead-market | |
| - real-time-market | |
| - spot-price | |
| - futures | |
| - forward-contracts | |
| - swaps | |
| - cfd | |
| - contract-for-differences | |
| - ancillary-services | |
| - regulation-up | |
| - regulation-down | |
| - spinning-reserve | |
| - black-start | |
| - voltage-support | |
| - bilateral-contracts | |
| - ppa | |
| - power-purchase-agreement | |
| - ote-energy | |
| - otc | |
| - market-clearing | |
| - pjm | |
| - caiso | |
| - ercot | |
| - miso | |
| - nyiso | |
| - iso-ne | |
| - ferc | |
| - nerc | |
| - ferc-order-755 | |
| - ferc-order-888 | |
| - ferc-order-890 | |
| - isda | |
| - eei-master-agreement | |
| - basel-iii | |
| - var | |
| - value-at-risk | |
| - cvar | |
| - conditional-var | |
| - risk-management | |
| - mean-reversion | |
| - ornstein-uhlenbeck | |
| - schwartz-model | |
| - options-greeks | |
| - implied-volatility | |
| - price-spike | |
| - negative-prices | |
| - capacity-market | |
| - demand-response | |
| - virtual-bidding | |
| - congestion | |
| - ftr | |
| - credit-rating | |
| pretty_name: ENR006 — Synthetic Wholesale Energy Market Trading Dataset (Sample) | |
| size_categories: | |
| - 10K<n<100K | |
| configs: | |
| - config_name: spot_price | |
| data_files: enr006_spot_price.parquet | |
| - config_name: futures_contracts | |
| data_files: enr006_futures_contracts.parquet | |
| - config_name: ancillary_services | |
| data_files: enr006_ancillary_services.parquet | |
| - config_name: market_clearing | |
| data_files: enr006_market_clearing.parquet | |
| - config_name: bilateral_contracts | |
| data_files: enr006_bilateral_contracts.parquet | |
| - config_name: trading_analytics | |
| data_files: enr006_trading_analytics.parquet | |
| # ENR006 — Synthetic Wholesale Energy Market Trading Dataset (Sample Preview) | |
| **XpertSystems.ai | Synthetic Data Factory | Energy & Climate Vertical** | |
| A **six-table wholesale energy market trading dataset** spanning the full | |
| trading lifecycle: hourly Day-Ahead LMPs (energy + congestion + loss | |
| three-part decomposition), futures / forwards / swaps / CfDs with options | |
| Greeks, six ancillary services markets (REG_UP, REG_DOWN, SPINNING_RESERVE, | |
| NON_SPIN_RESERVE, BLACK_START, VOLTAGE_SUPPORT), market clearing with | |
| imports/exports and energy balance, OTC bilateral PPAs with credit | |
| exposure, and per-trade execution analytics with Basel III coherent risk | |
| metrics (VaR-95, VaR-99, CVaR-95, Sharpe). Calibrated benchmark-first | |
| against **FERC Order 755/888/890**, **NERC reliability standards**, | |
| **ISDA Master Agreement**, **EEI Master Agreement**, **Basel III FRTB**, | |
| **Schwartz (1997) mean-reversion theory**, and **EIA/PJM/CAISO/ERCOT | |
| 2023 published LMP data**. | |
| This is the **sample preview** — 2 weeks (336 hours) of hourly DA market | |
| data + 500 futures + 300 bilateral + 2,000 trades + 1 week of ancillary | |
| services clearing (~13K total records). The full product covers a full | |
| annual cycle × 500 pricing nodes × 200 participants × 20K trades with | |
| pre-built scenario configs for price-spike events, high-renewable | |
| negative pricing, and capacity-crunch market stress. | |
| --- | |
| ## Dataset summary | |
| | Table | Rows (sample) | What it contains | | |
| |---|---:|---| | |
| | `spot_price` | 1,680 | Hourly DA LMP with three-part decomposition: lmp_total = energy + congestion + loss, plus system_lambda, peak/off-peak flags, weekend/holiday flag, price cap and negative price event flags | | |
| | `futures_contracts` | 500 | FUTURES / FORWARD_OTC / SWAP / CfD contracts: tenors (DAY/WEEK/MONTH/QUARTER/CALENDAR_YEAR), forward curve, basis, contract price, notional, options Greeks (delta/gamma/vega/theta), MTM, settlement P&L | | |
| | `ancillary_services` | ~8,500 | Hourly clearing for 6 services: clearing price, capacity awarded, performance score, mileage (REG_UP/DOWN), activation flag/duration, obligation, availability payment | | |
| | `market_clearing` | 336 | DAM clearing: total cleared load/gen/imports/exports, energy balance (zero by construction), reserve margin, convergence flag, virtual bid volume + P&L, interchange schedule, market surplus, demand response cleared, capacity market price | | |
| | `bilateral_contracts` | 300 | OTC PPAs: FIXED_PRICE / INDEXED / SHAPED / TOLLING structures, product type (FIRM / NONFIRM / UNIT_CONT / SYSTEM), volume, duration, fixed price, index reference, adder, total contract value, credit exposure, collateral posted, counterparty credit rating, EEI confirmation flag | | |
| | `trading_analytics` | 2,000 | Per-trade execution: timestamp (ms-precision), trader / book, BUY/SELL direction, quantity, execution and market price, slippage, transaction cost, realized + unrealized P&L, VaR_95, VaR_99, CVaR_95, Sharpe ratio, max drawdown, position, hedge ratio, regulatory flag | | |
| All six tables are provided in both **CSV** and **Parquet**. They join on | |
| `node_id`, `participant_id` (= buyer_id / seller_id / trader_id / | |
| provider_id), `book_id`, and `timestamp_utc`. | |
| --- | |
| ## Calibration sources | |
| All ten validation metrics target named industry sources, not generator | |
| self-metrics: | |
| - **FERC Order 888 / 890** — Open Access Transmission Tariff, LMP | |
| three-part decomposition (energy + congestion + loss) | |
| - **FERC Order 755** — Pay-for-performance regulation (REG_UP / REG_DOWN | |
| clearing structure) | |
| - **FERC Ancillary Services Tariffs** (PJM / CAISO / ERCOT 2023) — six | |
| ancillary product price ranges | |
| - **NERC TPL-001-5** — bulk system energy balance requirements | |
| - **NERC LOLP / IEEE Reliability Standards** — reserve margin planning | |
| ranges (12-25% typical, 5-40% observed) | |
| - **ISDA Master Agreement** — notional value definition for derivatives | |
| - **EEI Master Agreement** — bilateral power transaction value calculation | |
| - **Basel III FRTB** + **Artzner et al. (1999)** — coherent risk measure | |
| axioms (CVaR ≥ VaR, monotonicity in confidence level) | |
| - **EIA / PJM / CAISO / ERCOT 2023** — published wholesale hub LMP averages | |
| for cross-ISO calibration | |
| - **Schwartz (1997) / Lucia & Schwartz (2002)** — mean-reverting commodity | |
| price model theory | |
| --- | |
| ## 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 | `lmp_decomp_identity_normal_rows_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC Order 888/890 | | |
| | 2 | `market_clearing_energy_balance_zero_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | NERC TPL-001-5 | | |
| | 3 | `var_coherence_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Basel III / Artzner 1999 | | |
| | 4 | `futures_notional_identity_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ISDA Master Agreement | | |
| | 5 | `bilateral_total_value_identity_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | EEI Master Agreement | | |
| | 6 | `ancillary_clearing_prices_in_iso_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC AS tariffs | | |
| | 7 | `reserve_margin_in_industry_range_rate` | 1.000 | 0.95 | ±0.05 | FLOOR | NERC LOLP / IEEE | | |
| | 8 | `lmp_mean_usd_per_mwh_in_iso_band` | 40.26 | 45.0 | ±20.0 | two-sided | EIA/PJM/CAISO/ERCOT 2023 | | |
| | 9 | `reg_up_clearing_price_mean_usd_per_mw_hr` | 15.56 | 15.0 | ±5.0 | two-sided | FERC Order 755 / PJM REG-UP | | |
| | 10 | `spot_price_in_iso_floor_cap_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC/PJM Tariff | | |
| --- | |
| ## Schema highlights | |
| ### `spot_price` (1,680 rows × 13 cols) | |
| `node_id`, `timestamp_utc`, `settlement_type` (DAM), `lmp_total_usd_per_mwh`, | |
| `lmp_energy_usd_per_mwh`, `lmp_congestion_usd_per_mwh`, | |
| `lmp_loss_usd_per_mwh`, `system_lambda_usd_per_mwh`, `price_hub`, | |
| `hour_ending`, `peak_offpeak_flag` (ON_PEAK / OFF_PEAK), | |
| `weekend_holiday_flag`, `price_cap_flag`, `negative_price_flag`. | |
| ### `futures_contracts` (500 rows × 23 cols) | |
| `contract_id`, `contract_type` (FUTURES / FORWARD_OTC / SWAP / CfD), | |
| `underlying_hub`, `node_id`, `tenor` (DAY / WEEK / MONTH / QUARTER / | |
| CALENDAR_YEAR), `delivery_start_utc`, `delivery_end_utc`, | |
| `trade_date_utc`, `contract_price_usd_per_mwh`, | |
| `forward_curve_usd_per_mwh`, `basis_usd_per_mwh`, | |
| `contract_quantity_mwh`, `notional_value_usd`, `buyer_id`, `seller_id`, | |
| `trader_book`, `mark_to_market_usd_per_mwh`, `implied_vol_pct`, `delta`, | |
| `gamma`, `vega`, `theta`, `settlement_price_usd_per_mwh`, | |
| `settlement_gain_loss_usd`. | |
| ### `ancillary_services` (~8,500 rows × 13 cols) | |
| `ancillary_id`, `timestamp_utc`, `service_type` (REG_UP / REG_DOWN / | |
| SPINNING_RESERVE / NON_SPIN_RESERVE / BLACK_START / VOLTAGE_SUPPORT), | |
| `clearing_price_usd_per_mw_hr`, `capacity_awarded_mw`, | |
| `performance_score_pct`, `mileage_mw`, `mileage_payment_usd`, | |
| `activation_flag`, `activation_duration_min`, `provider_id`, `zone_id`, | |
| `obligation_mw`, `availability_payment_usd`. | |
| ### `market_clearing` (336 rows × 17 cols) | |
| `clearing_id`, `timestamp_utc`, `market_type` (DAM), | |
| `clearing_timestamp_utc`, `total_cleared_load_mw`, `total_cleared_gen_mw`, | |
| `total_cleared_imports_mw`, `total_cleared_exports_mw`, | |
| `energy_balance_mw`, `reserve_margin_pct`, `convergence_flag`, | |
| `virtual_bid_volume_mwh`, `virtual_bid_pnl_usd`, `interchange_schedule_mw`, | |
| `market_surplus_usd`, `demand_response_cleared_mw`, | |
| `capacity_market_price_usd_per_mw_day`, `system_lambda_usd_per_mwh`. | |
| ### `bilateral_contracts` (300 rows × 18 cols) | |
| `bilateral_id`, `trade_date_utc`, `contract_structure` (FIXED_PRICE / | |
| INDEXED / SHAPED / TOLLING), `buyer_id`, `seller_id`, `delivery_point`, | |
| `node_id`, `product_type` (FIRM / NONFIRM / UNIT_CONT / SYSTEM), | |
| `volume_mw`, `duration_months`, `fixed_price_usd_per_mwh`, | |
| `index_reference`, `adder_usd_per_mwh`, `total_contract_value_usd`, | |
| `credit_exposure_usd`, `collateral_posted_usd`, | |
| `counterparty_credit_rating` (AAA / AA / A / BBB / BB / B / CCC), | |
| `eei_confirmation_flag`. | |
| ### `trading_analytics` (2,000 rows × 19 cols) | |
| `trade_id`, `execution_timestamp_utc` (ms precision), `trader_id`, | |
| `book_id` (BOOK_01..BOOK_20), `trade_direction` (BUY / SELL), | |
| `trade_quantity_mwh`, `execution_price_usd_per_mwh`, | |
| `market_price_usd_per_mwh`, `slippage_usd_per_mwh`, | |
| `transaction_cost_usd`, `realized_pnl_usd`, `unrealized_pnl_usd`, | |
| `var_95_usd`, `var_99_usd`, `cvar_95_usd`, `sharpe_ratio`, | |
| `max_drawdown_usd`, `position_mw`, `hedge_ratio_pct`, | |
| `regulatory_flag`. | |
| --- | |
| ## Suggested use cases | |
| - **Day-ahead LMP forecasting** — train regressors / LSTMs for | |
| `lmp_total_usd_per_mwh` from time-of-day, day-of-year, peak/off-peak, | |
| and historical lag features | |
| - **Three-part LMP decomposition modeling** — predict | |
| `lmp_congestion_usd_per_mwh` and `lmp_loss_usd_per_mwh` separately from | |
| topology / loading proxies for FTR / CRR markets | |
| - **Price spike detection** — anomaly classifier for `price_cap_flag` and | |
| `negative_price_flag` from system_lambda, peak_offpeak_flag, and | |
| weather proxies (pair with ENR-002 weather data) | |
| - **Futures forward curve modeling** — fit yield-curve / forward-curve | |
| structures from `tenor`, `delivery_start_utc`, `contract_price`, | |
| `forward_curve` triples | |
| - **Options Greeks calibration** — train Black-76 / spread option models | |
| on `implied_vol_pct`, `delta`, `gamma`, `vega`, `theta` for | |
| options-on-futures pricing | |
| - **Ancillary services co-optimization** — joint price models for | |
| energy + AS clearing across 6 services | |
| - **Bilateral PPA pricing** — model `fixed_price_usd_per_mwh` as a | |
| function of `volume_mw`, `duration_months`, `counterparty_credit_rating`, | |
| and `index_reference`; useful for term-sheet automation | |
| - **Credit risk / counterparty exposure** — train default probability | |
| models from `counterparty_credit_rating` joined with | |
| `credit_exposure_usd` and `collateral_posted_usd` | |
| - **VaR backtesting** — use the included VaR_95 / VaR_99 / CVaR_95 columns | |
| as benchmarks for new ML-driven VaR models; check coherence axioms | |
| - **Slippage modeling** — predict `slippage_usd_per_mwh` from quantity, | |
| market_price, and time-of-day; useful for transaction cost analysis | |
| - **Virtual bidding (INC/DEC) strategies** — train signal models from | |
| `virtual_bid_volume_mwh` and `virtual_bid_pnl_usd` joined with LMP | |
| changes between DAM and RTM | |
| - **Regulatory flag detection** — multi-class for `regulatory_flag` from | |
| trade-level signals (quantity, slippage, market deviation); useful for | |
| market surveillance / spoofing detection | |
| - **Capacity market clearing modeling** — predict | |
| `capacity_market_price_usd_per_mw_day` from reserve_margin_pct and | |
| load growth trends | |
| - **Demand response clearing** — model `demand_response_cleared_mw` from | |
| LMP and load shape signals | |
| --- | |
| ## Loading examples | |
| ```python | |
| from datasets import load_dataset | |
| spot = load_dataset("xpertsystems/enr006-sample", "spot_price", split="train") | |
| futures = load_dataset("xpertsystems/enr006-sample", "futures_contracts", split="train") | |
| print(spot.shape, futures.shape) | |
| ``` | |
| ```python | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| spot = pd.read_parquet(hf_hub_download( | |
| "xpertsystems/enr006-sample", "enr006_spot_price.parquet", | |
| repo_type="dataset", | |
| )) | |
| # LMP three-part decomposition: verify the identity on non-cap, non-negative-price rows | |
| normal = spot[(spot["price_cap_flag"] == 0) & (spot["negative_price_flag"] == 0)] | |
| residual = ( | |
| normal["lmp_total_usd_per_mwh"] | |
| - normal["lmp_energy_usd_per_mwh"] | |
| - normal["lmp_congestion_usd_per_mwh"] | |
| - normal["lmp_loss_usd_per_mwh"] | |
| ).abs() | |
| print(f"Max decomp residual on normal rows: {residual.max():.6f}") | |
| print(f"Mean decomp residual: {residual.mean():.6f}") | |
| ``` | |
| ```python | |
| # Build a simple forward curve from futures | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| futures = pd.read_parquet(hf_hub_download( | |
| "xpertsystems/enr006-sample", "enr006_futures_contracts.parquet", | |
| repo_type="dataset", | |
| )) | |
| # Average price by tenor | |
| print(futures.groupby("tenor")["contract_price_usd_per_mwh"].agg(["mean", "std", "count"])) | |
| ``` | |
| ```python | |
| # Trader P&L attribution | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| trd = pd.read_parquet(hf_hub_download( | |
| "xpertsystems/enr006-sample", "enr006_trading_analytics.parquet", | |
| repo_type="dataset", | |
| )) | |
| book_pnl = trd.groupby("book_id").agg( | |
| realized=("realized_pnl_usd", "sum"), | |
| n_trades=("trade_id", "count"), | |
| avg_var95=("var_95_usd", "mean"), | |
| ).round(2).sort_values("realized", ascending=False) | |
| print(book_pnl.head(10)) | |
| ``` | |
| ```python | |
| # Validate VaR coherence (Basel III requirement) | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| trd = pd.read_parquet(hf_hub_download( | |
| "xpertsystems/enr006-sample", "enr006_trading_analytics.parquet", | |
| repo_type="dataset", | |
| )) | |
| var99_ge_var95 = (trd["var_99_usd"] >= trd["var_95_usd"]).mean() | |
| cvar95_ge_var95 = (trd["cvar_95_usd"] >= trd["var_95_usd"]).mean() | |
| print(f"VaR_99 >= VaR_95: {var99_ge_var95*100:.2f}%") | |
| print(f"CVaR_95 >= VaR_95: {cvar95_ge_var95*100:.2f}%") | |
| ``` | |
| --- | |
| ## Limitations and honest disclosures | |
| This sample is calibrated for **structural fidelity, not bit-exact reproduction | |
| of any specific ISO's settlement archive.** Specifically: | |
| - **Spot prices cover only 5 pricing nodes** even when `n_pricing_nodes` is | |
| set higher — the generator hardcodes `node_ids[:5]` in its main flow | |
| (line 821). This is intentional sample-mode behavior; the full product | |
| pipeline scales to 500+ pricing nodes via the per-node-batch design. | |
| - **Ancillary services covers only the first 168 timestamps** (one week | |
| via the generator's `timestamps_da[:168]` slice on line 823). For | |
| hours_da=336 in this sample, ancillary spans week 1 only; spot, futures, | |
| market clearing, and trading span the full 2-week window. | |
| - **LMP three-part decomposition is broken by design** on (a) rows where | |
| `lmp_total` is clamped to the ISO price cap or floor, and (b) rows where | |
| `negative_price_flag=1` (negative-price override). The wrapper validates | |
| the decomposition on NORMAL rows only (cap_flag=0 AND negative_price_flag=0). | |
| For research that requires the full identity to hold, mask out the | |
| special-case rows or use the `lmp_energy + lmp_congestion + lmp_loss` | |
| sum directly. | |
| - **Price spike rate and negative price rate are sample-scale unstable.** | |
| At 1,680 spot rows, the generator's Poisson(0.02) spike arrivals and | |
| the conjunction `random < 0.04 AND system_lambda < 0.3*base_lmp` | |
| for negative prices fire too rarely to validate against the generator's | |
| designed 2-10% spike / 1-8% negative-price targets. The full annual | |
| product matches those targets at scale. For tail-event ML, use the | |
| full product or the pre-built scenario configs. | |
| - **`system_lambda` AR(1) coefficient is HIGHLY seed-dependent at | |
| sample scale** (observed range 0.20-0.90 across 6 seeds). The underlying | |
| mean-reverting process has θ=0.12/hr → asymptotic AR(1) ≈ 0.88, but | |
| spike events at small samples distort the lag-1 correlation. We | |
| validate `system_lambda` falling within the ISO floor/cap bounds | |
| instead of the AR(1) coefficient. For mean-reversion analysis, use | |
| the full annual product or fit a state-space model. | |
| - **The generator's `run_benchmarks` reports "Grade: A+" misleadingly.** | |
| Its `all_passed` flag is only updated by `check_list` (line 598-603) | |
| which isn't actually invoked for any test in this version — so | |
| `all_passed=True` regardless of module-level pass/fail flags. This | |
| wrapper provides genuine industry-anchored validation via the | |
| scorecard above. | |
| - **Forward curve uses a simplified seasonal + linear-risk-premium | |
| shape** (`fwd_curve = mean(system_lambda) * seasonal_adj + | |
| risk_premium`). Real forward curves include calendar-spread structure, | |
| weather-stochastic vol, and counterparty-specific basis that the | |
| generator does not model. | |
| - **Options Greeks fire on only ~15% of contracts** (FUTURES + CfD types | |
| with 30% optionality probability). The remaining 85% have all Greeks | |
| set to zero. Filter `implied_vol_pct > 0` to extract the options- | |
| bearing subset before training Greek-prediction models. | |
| - **`negative_price_flag = 1` row prices use `-rng.exponential(20)`** — | |
| i.e., a magnitude draw, NOT a structural reason like solar oversupply | |
| or congestion island. Use the flag as a label, not a causal driver. | |
| - **`market_surplus_usd`, `virtual_bid_pnl_usd`, `capacity_market_price` | |
| are independent random draws**, not computed from underlying market | |
| dynamics. Treat as auxiliary fields for model-feature space, not as | |
| ground-truth market clearing outputs. | |
| - **Credit ratings are sampled with a designed distribution | |
| `[5%, 10%, 20%, 30%, 20%, 10%, 5%]` for [AAA, AA, A, BBB, BB, B, | |
| CCC]** — IG-skewed but not anchored to any specific counterparty | |
| pool. Use as a categorical feature; don't infer real-world default | |
| probabilities directly. | |
| - **All trades sampled from `participant_ids`** uniformly; trade pairings | |
| (buyer / seller) can occasionally match the same participant for both | |
| sides at small sample scale. For market-surveillance ML, filter | |
| buyer_id ≠ seller_id. | |
| - **`hours_da` is hourly cadence only** — no 5-min real-time market data | |
| in the sample. The full product includes both DAM and RTM at 5-min | |
| resolution via `intervals_rt_per_hour=12`. | |
| The full ENR006 product addresses these by full annual coverage, all | |
| 500+ pricing nodes, calibrated forward curves, RTM 5-min interval | |
| settlement, and pre-built scenario configs (price_spike_event, | |
| high_renewable_negative_prices, capacity_crunch, standard_annual) — | |
| contact us for the licensed commercial release. | |
| --- | |
| ## Companion datasets in the Energy & Climate vertical | |
| - **ENR-001** — Synthetic Power Grid Operations Dataset (transmission | |
| bus telemetry, line flows, generation dispatch, frequency, contingency) | |
| - **ENR-002** — Synthetic Renewable Energy Generation Dataset | |
| (utility-scale solar/wind/hybrid SCADA, weather, forecast, PCC, BESS) | |
| - **ENR-003** — Synthetic Electricity Demand & Load Forecasting Dataset | |
| (zone-level demand, multi-horizon forecasts, peak events, EV/DER, TOU) | |
| - **ENR-004** — Synthetic Upstream Oil & Gas Production Dataset | |
| (well-level production, decline curves, PVT, commodity prices, | |
| Subpart W methane) | |
| - **ENR-005** — Synthetic Smart Grid Dataset (AMI, DER, OpenADR, feeder | |
| power flow, grid edge analytics) | |
| - **ENR-006** — Synthetic Wholesale Energy Market Trading Dataset (you | |
| are here) — **the market/trading complement to ENR-001's physical-grid | |
| view**: spot price formation, derivatives, ancillary services, | |
| bilateral PPAs, and trading risk | |
| Use **ENR-001 + ENR-003 + ENR-006** together for full | |
| **physical-grid + load-forecast + market-clearing** ML workflows; combine | |
| with **ENR-002 + ENR-005** to add renewables and distribution-edge in | |
| the same modeling stack. | |
| For subsurface companion data (seismic, well logs, reservoir simulation, | |
| geological formations), see the **OIL series** (OIL-001 through OIL-004) | |
| in our [Oil & Gas vertical](https://huggingface.co/xpertsystems). | |
| For the broader catalog: | |
| - [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_enr006_sample_2026, | |
| author = {XpertSystems.ai}, | |
| title = {ENR006 Synthetic Wholesale Energy Market Trading Dataset (Sample Preview)}, | |
| year = 2026, | |
| publisher = {Hugging Face}, | |
| url = {https://huggingface.co/datasets/xpertsystems/enr006-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. | |