Evidence for the decision.

Probabilistic forecasting; chance constraints; asymmetric loss

Forecast hourly balancing-authority demand at a precisely defined issue time using lagged load, calendar information and only legitimately available inputs. Compare seasonal models and quantile boosting; benchmark against published demand forecasts only when their issue time aligns with the task. Weather forecasts are an optional, separately sourced extension.

Evaluation: Use rolling seasonal holdouts and an extreme-demand stress period. Report quantile loss, interval coverage, upper-tail exceedances and modeled reserve/shortfall cost relative to fixed margins. Record data vintages; label revised-data backtests when original releases are unavailable.

Boundary: Demand-only reserve scenarios omit generator outages and full network/security constraints. They are not an operational dispatch plan; do not use realized future weather or mismatched official-forecast timing.

EIA-930 Hourly Electric Grid Monitor ↗

Intertemporal substitution; panel effects; conditional DiD/ITT

Join household half-hourly consumption with the supplied 2013 price-signal calendar. Audit recruitment, group assignment and pre-period comparability before selecting the estimand. Use randomized assignment analysis only if verified; otherwise use household/time panel models and difference-in-differences with explicit identifying assumptions.

Evaluation: Predefine event windows, peak and adjacent-period outcomes. Compare with flat-tariff households, inspect pre-trends/placebo windows, cluster uncertainty by household and price event as appropriate, and assess heterogeneous effects using held-out households.

Boundary: A tariff-group label alone does not establish random assignment. If design documentation is insufficient, publish adjusted associations and sensitivity analyses rather than causal savings claims.

Low Carbon London SmartMeter data ↗

Global/local forecasting; partial pooling; peak quantiles

Build timezone-aware quarter-hourly series with explicit missing-value, zero and daylight-saving rules. Compare seasonal naive forecasts, local statistical models and a global quantile model. Cluster load shapes only within training data and evaluate whether cluster-aware models improve peak forecasting.

Evaluation: Use rolling time holdouts plus customer-held-out transfer tests. Report scaled error, peak-window quantile loss, interval coverage and worst-decile customer performance. Compare aggregate forecasts and sensitivity to DST handling and newly active customers.

Boundary: Customer identities and interventions are largely unknown. This benchmark cannot estimate causal tariff response or infer demographic explanations; capacity lines and operating costs are scenarios unless separately supplied.

ElectricityLoadDiagrams20112014 ↗