kalibrace per pole
This commit is contained in:
Dusan Vojacek
41
db/migration/V057__site_pv_forecast_calibration.sql
Normal file
41
db/migration/V057__site_pv_forecast_calibration.sql
Normal file
@@ -0,0 +1,41 @@
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-- Kalibrace PV forecastu per site (cutoff učení, škrcení policy, volitelné přepsání parametrů delty).
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-- forecast_accuracy: flagy pro učení (vyloučení škrcených slotů apod.).
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CREATE TABLE ems.site_pv_forecast_calibration (
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site_id int NOT NULL PRIMARY KEY REFERENCES ems.site (id) ON DELETE CASCADE,
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-- Od tohoto okamžiku (UTC) brát řádky do učení delty / vážených statistik (>=).
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delta_learn_min_ts timestamptz NOT NULL,
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-- Od kdy platí agresivní export/škrcení policy (NULL = neaplikovat časový filtr u heuristiky škrcení).
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pv_curtailment_policy_effective_from timestamptz NULL,
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top_n_days int NULL,
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non_top_day_factor numeric NULL,
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day_weight_gamma numeric NULL,
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half_life_days numeric NULL,
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threshold_w int NULL,
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updated_at timestamptz NOT NULL DEFAULT now()
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);
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COMMENT ON TABLE ems.site_pv_forecast_calibration IS
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'Per-site kalibrace PV delta profilu a pravidla učení. NULL v numerických sloupích = použít default z ems.fn_pv_forecast_delta_profile.';
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COMMENT ON COLUMN ems.site_pv_forecast_calibration.delta_learn_min_ts IS
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'Dolní mez interval_start pro učení delty z forecast_accuracy (UTC).';
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COMMENT ON COLUMN ems.site_pv_forecast_calibration.pv_curtailment_policy_effective_from IS
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'Od tohoto času bereme heuristiku škrcení (planning_interval): sloty po tomto datu s curtailment/cut-off se mohou vyloučit z učení.';
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ALTER TABLE ems.forecast_accuracy
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ADD COLUMN IF NOT EXISTS learning_eligible boolean NOT NULL DEFAULT true,
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ADD COLUMN IF NOT EXISTS learning_exclude_reason text NULL;
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COMMENT ON COLUMN ems.forecast_accuracy.learning_eligible IS
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'false = řádek se nepoužívá pro učení delty (škrcení, před cutoffem, …); actual_power_w může být NULL pro audit.';
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COMMENT ON COLUMN ems.forecast_accuracy.learning_exclude_reason IS
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'Důvod vyloučení z učení, např. curtailment_or_gen_cutoff, before_delta_learn_min.';
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-- Seed: všechny existující lokality — stejný cutoff jako dosud v R__078 (začátek 2026-04-12 Europe/Prague).
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INSERT INTO ems.site_pv_forecast_calibration (site_id, delta_learn_min_ts, top_n_days)
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SELECT s.id, timestamptz '2026-04-11T22:00:00Z', 3
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FROM ems.site s
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ON CONFLICT (site_id) DO NOTHING;
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@@ -12,7 +12,8 @@ BEGIN
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site_id, pv_array_id, interval_start, run_id,
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forecast_power_w, forecast_created_at, lead_time_hours,
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actual_power_w, actual_filled_at,
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error_w, error_pct
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error_w, error_pct,
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learning_eligible, learning_exclude_reason
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)
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SELECT
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fpr.site_id,
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@@ -25,10 +26,17 @@ BEGIN
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EXTRACT(EPOCH FROM (fpi.interval_start - fpr.created_at))
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/ 3600.0, 2
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) AS lead_time_hours,
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slot.avg_actual_w::INT AS actual_power_w,
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now() AS actual_filled_at,
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fpi.power_w - COALESCE(slot.avg_actual_w::INT, 0) AS error_w,
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CASE
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WHEN v.is_curtailed_learning_slot THEN NULL
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ELSE slot.avg_actual_w::INT
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END AS actual_power_w,
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now() AS actual_filled_at,
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CASE
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WHEN v.is_curtailed_learning_slot THEN NULL
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ELSE fpi.power_w - COALESCE(slot.avg_actual_w::INT, 0)
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END AS error_w,
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CASE
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WHEN v.is_curtailed_learning_slot THEN NULL
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WHEN slot.avg_actual_w IS NOT NULL
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AND slot.avg_actual_w > 0
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THEN ROUND(
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@@ -37,10 +45,62 @@ BEGIN
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4
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)
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ELSE NULL
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END AS error_pct
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END AS error_pct,
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v.learning_eligible,
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v.learning_exclude_reason
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FROM ems.forecast_pv_interval fpi
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JOIN ems.forecast_pv_run fpr ON fpr.id = fpi.run_id
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JOIN ems.asset_pv_array pa ON pa.id = fpr.pv_array_id
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LEFT JOIN ems.site_pv_forecast_calibration cal
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ON cal.site_id = fpr.site_id
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LEFT JOIN LATERAL (
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SELECT
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coalesce(cal.delta_learn_min_ts, timestamptz '2026-04-11T22:00:00Z') AS delta_learn_min_ts,
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cal.pv_curtailment_policy_effective_from AS policy_from
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) cal_eff ON true
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LEFT JOIN LATERAL (
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SELECT pi.pv_a_curtailed_w, pi.deye_gen_cutoff_enabled
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FROM ems.planning_interval pi
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JOIN ems.planning_run pr ON pr.id = pi.run_id
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WHERE pr.site_id = fpr.site_id
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AND pr.status = 'active'
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AND pi.interval_start = fpi.interval_start
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LIMIT 1
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) ap ON true
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LEFT JOIN LATERAL (
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SELECT
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(fpi.interval_start < cal_eff.delta_learn_min_ts) AS before_learn_cutoff,
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(
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cal_eff.policy_from IS NOT NULL
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AND fpi.interval_start >= cal_eff.policy_from
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AND (
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coalesce(ap.pv_a_curtailed_w, 0) > 50
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OR coalesce(ap.deye_gen_cutoff_enabled, false) IS TRUE
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OR EXISTS (
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SELECT 1
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FROM ems.cutoff_switch_log l
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WHERE l.site_id = fpr.site_id
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AND l.switched_at >= fpi.interval_start
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AND l.switched_at < fpi.interval_start + INTERVAL '15 minutes'
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AND l.new_state IS FALSE
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)
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)
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) AS is_curtailed_learning_slot
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) flags ON true
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LEFT JOIN LATERAL (
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SELECT
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CASE
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WHEN flags.before_learn_cutoff THEN false
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WHEN flags.is_curtailed_learning_slot THEN false
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ELSE true
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END AS learning_eligible,
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CASE
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WHEN flags.before_learn_cutoff THEN 'before_delta_learn_min'
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WHEN flags.is_curtailed_learning_slot THEN 'curtailment_or_export_cutoff'
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ELSE NULL
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END AS learning_exclude_reason,
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flags.is_curtailed_learning_slot
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) v ON true
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LEFT JOIN LATERAL (
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SELECT AVG(
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CASE
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@@ -58,10 +118,12 @@ BEGIN
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AND fpi.interval_start < now() - INTERVAL '15 minutes'
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AND fpi.interval_start >= now() - make_interval(hours => p_lookback_hours)
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ON CONFLICT (run_id, interval_start) DO UPDATE SET
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actual_power_w = EXCLUDED.actual_power_w,
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actual_filled_at = EXCLUDED.actual_filled_at,
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error_w = EXCLUDED.error_w,
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error_pct = EXCLUDED.error_pct;
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actual_power_w = EXCLUDED.actual_power_w,
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actual_filled_at = EXCLUDED.actual_filled_at,
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error_w = EXCLUDED.error_w,
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error_pct = EXCLUDED.error_pct,
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learning_eligible = EXCLUDED.learning_eligible,
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learning_exclude_reason = EXCLUDED.learning_exclude_reason;
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GET DIAGNOSTICS v_count = ROW_COUNT;
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RETURN v_count;
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@@ -70,6 +132,8 @@ $$;
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COMMENT ON FUNCTION ems.fn_fill_forecast_accuracy(INT, INT) IS
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'Doplní skutečné hodnoty výroby do forecast_accuracy z telemetrie.
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learning_eligible / learning_exclude_reason: před delta_learn_min_ts (kalibrace site) se nepočítá do učení delty;
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po pv_curtailment_policy_effective_from sloty s curtailment / gen cutoff / cutoff_switch_log (export off) mají NULL actual a jsou vyloučeny z učení.
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Volat každých 15 minut (spolu s audit_filler) pro inkrementální plnění.
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p_lookback_hours: kolik hodin zpět zpracovat (default 48h pro catch-up).
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Pro první backfill: SELECT ems.fn_fill_forecast_accuracy(2, 8760) -- 1 rok';
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@@ -44,7 +44,28 @@ declare
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begin
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drop table if exists _ems_plan_slot_wk;
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create temp table _ems_plan_slot_wk on commit drop as
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with slot_spine as (
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with prof as (
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select ems.fn_pv_forecast_delta_profile(
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p_site_id,
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greatest(p_from, now() - interval '120 days'),
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now()
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) as j
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),
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delta_unnest as (
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select (kv.key)::int as pv_array_id,
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(x->>'slot_of_day')::int as slot_of_day,
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(x->>'delta_w')::int as delta_w
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from prof
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cross join lateral jsonb_each((prof.j)->'deltas_by_array') kv(key, value)
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cross join lateral jsonb_array_elements(kv.value->'deltas') x
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),
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legacy_slot_delta as (
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select (x->>'slot_of_day')::int as slot_of_day,
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(x->>'delta_w')::int as delta_w
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from prof
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cross join lateral jsonb_array_elements((prof.j)->'deltas') x
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),
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slot_spine as (
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select gs as interval_start
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from generate_series(
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p_from,
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@@ -108,9 +129,9 @@ begin
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left join ems.vw_site_effective_price ep
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on ep.site_id = p_site_id and ep.interval_start = s.interval_start
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left join lateral (
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select coalesce(sum(u.power_w), 0)::int as power_w
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from (
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with uq as (
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select distinct on (apa.id)
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apa.id as pv_array_id,
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fpi.power_w
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from ems.asset_pv_array apa
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join ems.forecast_pv_run fpr
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@@ -124,12 +145,42 @@ begin
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where apa.site_id = p_site_id
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and apa.controllable is true
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order by apa.id, fpr.created_at desc
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) u
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),
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slot_of as (
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select (
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(extract(hour from (s.interval_start at time zone 'Europe/Prague'))::int * 60)
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+ extract(minute from (s.interval_start at time zone 'Europe/Prague'))::int
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) / 15 as slot_of_day
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),
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tot as (select coalesce(sum(uq.power_w), 0)::numeric as w from uq)
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select coalesce(sum(
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greatest(
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0,
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uq.power_w - coalesce(
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du.delta_w,
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case
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when exists (select 1 from delta_unnest limit 1) then null
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else round(
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ld.delta_w::numeric * uq.power_w::numeric / nullif((select w from tot), 0)
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)::int
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end,
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0
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)
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)
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), 0)::int as power_w
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from uq
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cross join slot_of
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cross join tot
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left join delta_unnest du
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on du.pv_array_id = uq.pv_array_id
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and du.slot_of_day = slot_of.slot_of_day
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left join legacy_slot_delta ld
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on ld.slot_of_day = slot_of.slot_of_day
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) fpi_a on true
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left join lateral (
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select coalesce(sum(u.power_w), 0)::int as power_w
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from (
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with uq as (
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select distinct on (apa.id)
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apa.id as pv_array_id,
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fpi.power_w
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from ems.asset_pv_array apa
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join ems.forecast_pv_run fpr
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@@ -143,7 +194,37 @@ begin
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where apa.site_id = p_site_id
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and apa.controllable is false
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order by apa.id, fpr.created_at desc
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) u
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),
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slot_of as (
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select (
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(extract(hour from (s.interval_start at time zone 'Europe/Prague'))::int * 60)
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+ extract(minute from (s.interval_start at time zone 'Europe/Prague'))::int
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) / 15 as slot_of_day
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),
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tot as (select coalesce(sum(uq.power_w), 0)::numeric as w from uq)
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select coalesce(sum(
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greatest(
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0,
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uq.power_w - coalesce(
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du.delta_w,
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case
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when exists (select 1 from delta_unnest limit 1) then null
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else round(
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ld.delta_w::numeric * uq.power_w::numeric / nullif((select w from tot), 0)
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)::int
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end,
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0
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)
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)
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), 0)::int as power_w
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from uq
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cross join slot_of
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cross join tot
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left join delta_unnest du
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on du.pv_array_id = uq.pv_array_id
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and du.slot_of_day = slot_of.slot_of_day
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left join legacy_slot_delta ld
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on ld.slot_of_day = slot_of.slot_of_day
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) fpi_b on true
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left join lateral (
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select t.status
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@@ -1,136 +1,153 @@
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-- ============================================================
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-- Profil systematické chyby PV forecastu po 15min slotu dne
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-- (aditivní korekce: corrected = max(0, forecast - delta[slot]))
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-- (aditivní korekce per pole: corrected_i = max(0, forecast_i - delta_i[slot]))
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-- + součtový profil `deltas` pro starší klienty (součet delt přes pole).
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-- ============================================================
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drop function if exists ems.fn_pv_forecast_delta_profile;
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DROP FUNCTION IF EXISTS ems.fn_pv_forecast_delta_profile;
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create or replace function ems.fn_pv_forecast_delta_profile(
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CREATE OR REPLACE FUNCTION ems.fn_pv_forecast_delta_profile(
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p_site_id int,
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p_data_from timestamptz,
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p_data_to timestamptz default now(),
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p_half_life_days numeric default 14,
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p_threshold_w int default 150,
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p_top_n_days int default 2,
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p_non_top_day_factor numeric default 0.02,
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p_day_weight_gamma numeric default 1.0
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p_data_to timestamptz DEFAULT now(),
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p_half_life_days numeric DEFAULT 14,
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p_threshold_w int DEFAULT 150,
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p_top_n_days int DEFAULT 3,
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p_non_top_day_factor numeric DEFAULT 0.02,
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p_day_weight_gamma numeric DEFAULT 1.0
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)
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returns jsonb
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language sql
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stable
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as $fn$
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with tz as (
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select coalesce(nullif(trim(s.timezone), ''), 'Europe/Prague') as tz_name
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from ems.site s
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where s.id = p_site_id
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RETURNS jsonb
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LANGUAGE sql
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STABLE
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AS $fn$
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WITH eff AS (
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SELECT
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coalesce(cal.delta_learn_min_ts, timestamptz '2026-04-11T22:00:00Z') AS delta_learn_min_ts,
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coalesce(cal.half_life_days, p_half_life_days) AS half_life_days,
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coalesce(cal.threshold_w, p_threshold_w) AS threshold_w,
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coalesce(cal.top_n_days, p_top_n_days) AS top_n_days,
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coalesce(cal.non_top_day_factor, p_non_top_day_factor) AS non_top_day_factor,
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coalesce(cal.day_weight_gamma, p_day_weight_gamma) AS day_weight_gamma
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FROM ems.site s
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LEFT JOIN ems.site_pv_forecast_calibration cal ON cal.site_id = s.id
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WHERE s.id = p_site_id
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),
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-- Cutoff: učení delty jen od začátku kalendářního dne 2026-04-12 (Europe/Prague).
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-- (UTC okamžik odpovídá DST v dubnu: půlnoc v Praze = předchozí den 22:00 UTC.)
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-- Před tím mohou být v `forecast_accuracy` nekonzistentní historická data (telemetrie signed/unsigned).
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cutoff as (
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select timestamptz '2026-04-11T22:00:00Z' as min_ts
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tz AS (
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SELECT coalesce(nullif(trim(s.timezone), ''), 'Europe/Prague') AS tz_name
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FROM ems.site s
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WHERE s.id = p_site_id
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),
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bounds as (
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select
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greatest(p_data_from, p_data_to - interval '120 days', (select min_ts from cutoff)) as ts_from,
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p_data_to as ts_to,
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greatest(p_half_life_days, 1) as half_life_days,
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greatest(p_threshold_w, 0) as threshold_w
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bounds AS (
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SELECT
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greatest(
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p_data_from,
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p_data_to - interval '120 days',
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(SELECT delta_learn_min_ts FROM eff)
|
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) AS ts_from,
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p_data_to AS ts_to,
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greatest((SELECT half_life_days FROM eff), 1::numeric) AS half_life_days,
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greatest((SELECT threshold_w FROM eff), 0::numeric) AS threshold_w
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),
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-- vezmeme jeden „reprezentativní“ forecast z historie: pro každý interval_start a pv_array_id
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-- vybereme nejnovější forecast (forecast_created_at) který je <= interval_start (lead_time >= 0)
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best as (
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select
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best AS (
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SELECT
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||||
fa.interval_start,
|
||||
fa.pv_array_id,
|
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fa.forecast_power_w,
|
||||
fa.actual_power_w,
|
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fa.forecast_created_at,
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row_number() over (
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partition by fa.interval_start, fa.pv_array_id
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order by fa.forecast_created_at desc
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) as rn
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||||
from ems.forecast_accuracy fa
|
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cross join bounds b
|
||||
where fa.site_id = p_site_id
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||||
and fa.interval_start >= b.ts_from
|
||||
and fa.interval_start < b.ts_to
|
||||
and fa.actual_power_w is not null
|
||||
and fa.forecast_created_at <= fa.interval_start
|
||||
row_number() OVER (
|
||||
PARTITION BY fa.interval_start, fa.pv_array_id
|
||||
ORDER BY fa.forecast_created_at DESC
|
||||
) AS rn
|
||||
FROM ems.forecast_accuracy fa
|
||||
CROSS JOIN bounds b
|
||||
WHERE fa.site_id = p_site_id
|
||||
AND fa.interval_start >= b.ts_from
|
||||
AND fa.interval_start < b.ts_to
|
||||
AND fa.actual_power_w IS NOT NULL
|
||||
AND fa.forecast_created_at <= fa.interval_start
|
||||
AND coalesce(fa.learning_eligible, true) IS TRUE
|
||||
),
|
||||
slots as (
|
||||
select
|
||||
slots AS (
|
||||
SELECT
|
||||
b.interval_start,
|
||||
sum(b.forecast_power_w)::numeric as forecast_total_w,
|
||||
sum(b.actual_power_w)::numeric as actual_total_w,
|
||||
b.pv_array_id,
|
||||
b.forecast_power_w::numeric AS forecast_w,
|
||||
b.actual_power_w::numeric AS actual_w,
|
||||
(
|
||||
(extract(hour from (b.interval_start at time zone tz.tz_name))::int * 60)
|
||||
+ extract(minute from (b.interval_start at time zone tz.tz_name))::int
|
||||
) / 15 as slot_of_day,
|
||||
(b.interval_start at time zone tz.tz_name)::date as day_local,
|
||||
extract(epoch from (now() - b.interval_start)) / 86400.0 as age_days
|
||||
from best b
|
||||
cross join tz
|
||||
where b.rn = 1
|
||||
group by b.interval_start, slot_of_day, day_local, tz.tz_name
|
||||
(extract(hour FROM (b.interval_start AT TIME ZONE tz.tz_name))::int * 60)
|
||||
+ extract(minute FROM (b.interval_start AT TIME ZONE tz.tz_name))::int
|
||||
) / 15 AS slot_of_day,
|
||||
(b.interval_start AT TIME ZONE tz.tz_name)::date AS day_local,
|
||||
extract(epoch FROM (now() - b.interval_start)) / 86400.0 AS age_days
|
||||
FROM best b
|
||||
CROSS JOIN tz
|
||||
WHERE b.rn = 1
|
||||
),
|
||||
-- Denní „clear-ish“ skóre: preferujeme dny s hladkou křivkou výroby a vysokou denní energií
|
||||
-- relativně k ostatním dnům v okně (mraky dělají vysokofrekvenční šum na 15min, který není dobrý anchor pro slot bias).
|
||||
day_energy as (
|
||||
select
|
||||
slot_totals AS (
|
||||
SELECT
|
||||
s.interval_start,
|
||||
s.day_local,
|
||||
sum(s.actual_total_w)::numeric / 4000.0 as energy_kwh
|
||||
from slots s
|
||||
group by s.day_local
|
||||
s.slot_of_day,
|
||||
max(s.age_days) AS age_days,
|
||||
sum(s.forecast_w) AS forecast_total_w,
|
||||
sum(s.actual_w) AS actual_total_w
|
||||
FROM slots s
|
||||
GROUP BY s.interval_start, s.day_local, s.slot_of_day
|
||||
),
|
||||
ref as (
|
||||
select percentile_cont(0.5) within group (order by de.energy_kwh) as med_kwh
|
||||
from day_energy de
|
||||
day_energy AS (
|
||||
SELECT st.day_local, sum(st.actual_total_w)::numeric / 4000.0 AS energy_kwh
|
||||
FROM slot_totals st
|
||||
GROUP BY st.day_local
|
||||
),
|
||||
slot_steps as (
|
||||
select
|
||||
s.*,
|
||||
lag(s.actual_total_w) over (partition by s.day_local order by s.interval_start) as prev_actual_w
|
||||
from slots s
|
||||
where s.slot_of_day between 20 and 80
|
||||
and s.actual_total_w > (select threshold_w from bounds)
|
||||
ref AS (
|
||||
SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY de.energy_kwh) AS med_kwh
|
||||
FROM day_energy de
|
||||
),
|
||||
day_jump as (
|
||||
select
|
||||
slot_steps AS (
|
||||
SELECT
|
||||
st.*,
|
||||
lag(st.actual_total_w) OVER (PARTITION BY st.day_local ORDER BY st.interval_start) AS prev_actual_w
|
||||
FROM slot_totals st
|
||||
WHERE st.slot_of_day BETWEEN 20 AND 80
|
||||
AND st.actual_total_w > (SELECT threshold_w FROM bounds)
|
||||
),
|
||||
day_jump AS (
|
||||
SELECT
|
||||
ss.day_local,
|
||||
percentile_cont(0.5) within group (order by abs(ss.actual_total_w - ss.prev_actual_w)) as med_jump_w
|
||||
from slot_steps ss
|
||||
where ss.prev_actual_w is not null
|
||||
group by ss.day_local
|
||||
percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(ss.actual_total_w - ss.prev_actual_w)) AS med_jump_w
|
||||
FROM slot_steps ss
|
||||
WHERE ss.prev_actual_w IS NOT NULL
|
||||
GROUP BY ss.day_local
|
||||
),
|
||||
day_med as (
|
||||
select
|
||||
s.day_local,
|
||||
percentile_cont(0.5) within group (order by s.actual_total_w) as p50_actual_w
|
||||
from slots s
|
||||
where s.actual_total_w > (select threshold_w from bounds)
|
||||
group by s.day_local
|
||||
day_med AS (
|
||||
SELECT
|
||||
st.day_local,
|
||||
percentile_cont(0.5) WITHIN GROUP (ORDER BY st.actual_total_w) AS p50_actual_w
|
||||
FROM slot_totals st
|
||||
WHERE st.actual_total_w > (SELECT threshold_w FROM bounds)
|
||||
GROUP BY st.day_local
|
||||
),
|
||||
day_stats as (
|
||||
select
|
||||
day_stats AS (
|
||||
SELECT
|
||||
de.day_local,
|
||||
de.energy_kwh,
|
||||
dj.med_jump_w,
|
||||
dm.p50_actual_w,
|
||||
case
|
||||
when (select med_kwh from ref) is null or (select med_kwh from ref) <= 0 then 0.5
|
||||
else greatest(
|
||||
CASE
|
||||
WHEN (SELECT med_kwh FROM ref) IS NULL OR (SELECT med_kwh FROM ref) <= 0 THEN 0.5
|
||||
ELSE greatest(
|
||||
0.0,
|
||||
least(
|
||||
1.0,
|
||||
(de.energy_kwh - (select med_kwh from ref) * 0.55)
|
||||
/ nullif((select med_kwh from ref) * 0.35, 0)
|
||||
(de.energy_kwh - (SELECT med_kwh FROM ref) * 0.55)
|
||||
/ nullif((SELECT med_kwh FROM ref) * 0.35, 0)
|
||||
)
|
||||
)
|
||||
end as w_energy,
|
||||
case
|
||||
when dj.med_jump_w is null or dm.p50_actual_w is null then 0.35
|
||||
else greatest(
|
||||
END AS w_energy,
|
||||
CASE
|
||||
WHEN dj.med_jump_w IS NULL OR dm.p50_actual_w IS NULL THEN 0.35
|
||||
ELSE greatest(
|
||||
0.0,
|
||||
least(
|
||||
1.0,
|
||||
@@ -141,34 +158,34 @@ as $fn$
|
||||
)
|
||||
)
|
||||
)
|
||||
end as w_smooth
|
||||
from day_energy de
|
||||
left join day_jump dj on dj.day_local = de.day_local
|
||||
left join day_med dm on dm.day_local = de.day_local
|
||||
END AS w_smooth
|
||||
FROM day_energy de
|
||||
LEFT JOIN day_jump dj ON dj.day_local = de.day_local
|
||||
LEFT JOIN day_med dm ON dm.day_local = de.day_local
|
||||
),
|
||||
-- Volitelně: jen top N kalendářních dní podle (w_energy * w_smooth); zbytek ztlumit (bez hardcodu data).
|
||||
day_rank as (
|
||||
select
|
||||
day_rank AS (
|
||||
SELECT
|
||||
ds.day_local,
|
||||
row_number() over (
|
||||
order by
|
||||
(coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35)) desc,
|
||||
ds.day_local desc
|
||||
) as rn
|
||||
from day_stats ds
|
||||
row_number() OVER (
|
||||
ORDER BY
|
||||
(coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35)) DESC,
|
||||
ds.day_local DESC
|
||||
) AS rn
|
||||
FROM day_stats ds
|
||||
),
|
||||
filtered as (
|
||||
select
|
||||
filtered AS (
|
||||
SELECT
|
||||
s.pv_array_id,
|
||||
s.slot_of_day,
|
||||
(s.forecast_total_w - s.actual_total_w) as error_w,
|
||||
exp(-s.age_days / nullif((select half_life_days from bounds), 0))
|
||||
(s.forecast_w - s.actual_w) AS error_w,
|
||||
exp(-s.age_days / nullif((SELECT half_life_days FROM bounds), 0))
|
||||
* (
|
||||
case
|
||||
when p_top_n_days is null then 1::numeric
|
||||
when p_top_n_days < 1 then 1::numeric
|
||||
when dr.rn <= p_top_n_days then 1::numeric
|
||||
else greatest(0::numeric, least(1::numeric, coalesce(p_non_top_day_factor, 0.02)))
|
||||
end
|
||||
CASE
|
||||
WHEN (SELECT top_n_days FROM eff) IS NULL THEN 1::numeric
|
||||
WHEN (SELECT top_n_days FROM eff) < 1 THEN 1::numeric
|
||||
WHEN dr.rn <= (SELECT top_n_days FROM eff) THEN 1::numeric
|
||||
ELSE greatest(0::numeric, least(1::numeric, coalesce((SELECT non_top_day_factor FROM eff), 0.02)))
|
||||
END
|
||||
)
|
||||
* (
|
||||
0.05
|
||||
@@ -178,53 +195,93 @@ as $fn$
|
||||
0.0,
|
||||
least(1.0, coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35))
|
||||
),
|
||||
greatest(0.25, least(coalesce(p_day_weight_gamma, 1.0), 8.0))
|
||||
greatest(0.25, least(coalesce((SELECT day_weight_gamma FROM eff), 1.0), 8.0))
|
||||
)
|
||||
) as w
|
||||
from slots s
|
||||
cross join bounds b
|
||||
left join day_stats ds on ds.day_local = s.day_local
|
||||
left join day_rank dr on dr.day_local = s.day_local
|
||||
where s.slot_of_day between 0 and 95
|
||||
and (s.actual_total_w > b.threshold_w or s.forecast_total_w > b.threshold_w)
|
||||
) AS w
|
||||
FROM slots s
|
||||
CROSS JOIN bounds b
|
||||
CROSS JOIN eff
|
||||
JOIN slot_totals st ON st.interval_start = s.interval_start
|
||||
LEFT JOIN day_stats ds ON ds.day_local = s.day_local
|
||||
LEFT JOIN day_rank dr ON dr.day_local = s.day_local
|
||||
WHERE s.slot_of_day BETWEEN 0 AND 95
|
||||
AND (s.actual_w > b.threshold_w OR s.forecast_w > b.threshold_w)
|
||||
),
|
||||
agg as (
|
||||
select
|
||||
slot_of_day,
|
||||
count(*) as sample_count,
|
||||
sum(w) as w_sum,
|
||||
case
|
||||
when sum(w) > 0 then sum(error_w * w) / sum(w)
|
||||
else null
|
||||
end as delta_w
|
||||
from filtered
|
||||
group by slot_of_day
|
||||
agg_by_array AS (
|
||||
SELECT
|
||||
f.pv_array_id,
|
||||
f.slot_of_day,
|
||||
count(*) AS sample_count,
|
||||
sum(f.w) AS w_sum,
|
||||
CASE
|
||||
WHEN sum(f.w) > 0 THEN sum(f.error_w * f.w) / sum(f.w)
|
||||
ELSE NULL
|
||||
END AS delta_w
|
||||
FROM filtered f
|
||||
GROUP BY f.pv_array_id, f.slot_of_day
|
||||
),
|
||||
spine as (
|
||||
select generate_series(0, 95) as slot_of_day
|
||||
agg_total AS (
|
||||
SELECT
|
||||
sp.slot_of_day,
|
||||
sum(coalesce(ab.sample_count, 0))::bigint AS sample_count,
|
||||
sum(coalesce(round(ab.delta_w)::int, 0))::int AS delta_w
|
||||
FROM generate_series(0, 95) AS sp(slot_of_day)
|
||||
LEFT JOIN agg_by_array ab ON ab.slot_of_day = sp.slot_of_day
|
||||
GROUP BY sp.slot_of_day
|
||||
),
|
||||
arrays_block AS (
|
||||
SELECT coalesce(jsonb_object_agg(apa.id::text, arr.pack), '{}'::jsonb) AS deltas_by_array
|
||||
FROM ems.asset_pv_array apa
|
||||
CROSS JOIN LATERAL (
|
||||
SELECT jsonb_build_object(
|
||||
'deltas',
|
||||
coalesce(
|
||||
jsonb_agg(
|
||||
jsonb_build_object(
|
||||
'slot_of_day', sp.slot_of_day,
|
||||
'delta_w', coalesce(round(a.delta_w)::int, 0),
|
||||
'sample_count', coalesce(a.sample_count, 0)
|
||||
)
|
||||
ORDER BY sp.slot_of_day
|
||||
),
|
||||
'[]'::jsonb
|
||||
)
|
||||
) AS pack
|
||||
FROM generate_series(0, 95) AS sp(slot_of_day)
|
||||
LEFT JOIN agg_by_array a
|
||||
ON a.pv_array_id = apa.id
|
||||
AND a.slot_of_day = sp.slot_of_day
|
||||
) arr
|
||||
WHERE apa.site_id = p_site_id
|
||||
),
|
||||
spine AS (
|
||||
SELECT generate_series(0, 95) AS slot_of_day
|
||||
)
|
||||
select jsonb_build_object(
|
||||
SELECT jsonb_build_object(
|
||||
'site_id', p_site_id,
|
||||
'data_from', (select ts_from from bounds),
|
||||
'data_to', (select ts_to from bounds),
|
||||
'half_life_days', (select half_life_days from bounds),
|
||||
'threshold_w', (select threshold_w from bounds),
|
||||
'data_from', (SELECT ts_from FROM bounds),
|
||||
'data_to', (SELECT ts_to FROM bounds),
|
||||
'delta_learn_min_ts', (SELECT delta_learn_min_ts FROM eff),
|
||||
'half_life_days', (SELECT half_life_days FROM bounds),
|
||||
'threshold_w', (SELECT threshold_w FROM bounds),
|
||||
'top_n_days', (SELECT top_n_days FROM eff),
|
||||
'deltas',
|
||||
coalesce(
|
||||
jsonb_agg(
|
||||
jsonb_build_object(
|
||||
'slot_of_day', sp.slot_of_day,
|
||||
'delta_w', coalesce(round(a.delta_w)::int, 0),
|
||||
'sample_count', coalesce(a.sample_count, 0)
|
||||
'delta_w', coalesce(at.delta_w, 0),
|
||||
'sample_count', coalesce(at.sample_count, 0)
|
||||
)
|
||||
order by sp.slot_of_day
|
||||
ORDER BY sp.slot_of_day
|
||||
),
|
||||
'[]'::jsonb
|
||||
)
|
||||
),
|
||||
'deltas_by_array', (SELECT deltas_by_array FROM arrays_block)
|
||||
)
|
||||
from spine sp
|
||||
left join agg a on a.slot_of_day = sp.slot_of_day;
|
||||
FROM spine sp
|
||||
LEFT JOIN agg_total at ON at.slot_of_day = sp.slot_of_day;
|
||||
$fn$;
|
||||
|
||||
comment on function ems.fn_pv_forecast_delta_profile is
|
||||
'Aditivní delta profil chyby PV forecastu po 15min slotu dne (96 slotů). Zdroj: forecast_accuracy, vážení exp(-age/half_life_days) * day_weight (clear-ish dny) * top_n_days (default 3 = jen 3 nejlepší kalendářní dny podle w_energy*w_smooth, ostatní ztlumené non_top_day_factor; explicitní NULL = tier vypnut, váží se všechny dny) * power(day_weight, day_weight_gamma). Vrací JSON {deltas:[{slot_of_day, delta_w, sample_count}], ...}. Cutoff dat od 2026-04-12 Europe/Prague.';
|
||||
COMMENT ON FUNCTION ems.fn_pv_forecast_delta_profile IS
|
||||
'Aditivní delta profil PV forecastu po 15min slotu dne (96 slotů) per pv_array_id v `deltas_by_array`; `deltas` je součet delt přes pole (kompatibilita). Zdroj: forecast_accuracy s learning_eligible, cutoff a numerické defaulty z ems.site_pv_forecast_calibration (NULL sloupce = parametry volání).';
|
||||
|
||||
@@ -1,125 +1,156 @@
|
||||
-- ============================================================
|
||||
-- PV forecast sloty (15min) + aditivně korigovaný forecast
|
||||
-- corrected = max(0, forecast - delta_profile[slot_of_day])
|
||||
-- corrected = sum_i max(0, forecast_i - delta_profile_i[slot_of_day])
|
||||
-- ============================================================
|
||||
|
||||
drop function if exists ems.fn_forecast_pv_slots_range_corrected;
|
||||
DROP FUNCTION IF EXISTS ems.fn_forecast_pv_slots_range_corrected;
|
||||
|
||||
create or replace function ems.fn_forecast_pv_slots_range_corrected(
|
||||
CREATE OR REPLACE FUNCTION ems.fn_forecast_pv_slots_range_corrected(
|
||||
p_site_id int,
|
||||
p_from timestamptz,
|
||||
p_to timestamptz,
|
||||
p_delta_data_from timestamptz,
|
||||
p_delta_data_to timestamptz default now(),
|
||||
p_half_life_days numeric default 14,
|
||||
p_threshold_w int default 150
|
||||
p_delta_data_to timestamptz DEFAULT now(),
|
||||
p_half_life_days numeric DEFAULT 14,
|
||||
p_threshold_w int DEFAULT 150
|
||||
)
|
||||
returns jsonb
|
||||
language sql
|
||||
stable
|
||||
as $fn$
|
||||
with tz as (
|
||||
select coalesce(nullif(trim(s.timezone), ''), 'Europe/Prague') as tz_name
|
||||
from ems.site s
|
||||
where s.id = p_site_id
|
||||
RETURNS jsonb
|
||||
LANGUAGE sql
|
||||
STABLE
|
||||
AS $fn$
|
||||
WITH tz AS (
|
||||
SELECT coalesce(nullif(trim(s.timezone), ''), 'Europe/Prague') AS tz_name
|
||||
FROM ems.site s
|
||||
WHERE s.id = p_site_id
|
||||
),
|
||||
bounds as (
|
||||
select
|
||||
date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') as ts_from,
|
||||
case
|
||||
when p_to <= p_from then date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') + interval '15 minutes'
|
||||
when p_to > p_from + interval '60 days' then date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') + interval '60 days'
|
||||
else date_bin(interval '15 minutes', p_to, timestamptz '1970-01-01T00:00:00Z')
|
||||
end as ts_to
|
||||
bounds AS (
|
||||
SELECT
|
||||
date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') AS ts_from,
|
||||
CASE
|
||||
WHEN p_to <= p_from THEN date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') + interval '15 minutes'
|
||||
WHEN p_to > p_from + interval '60 days' THEN date_bin(interval '15 minutes', p_from, timestamptz '1970-01-01T00:00:00Z') + interval '60 days'
|
||||
ELSE date_bin(interval '15 minutes', p_to, timestamptz '1970-01-01T00:00:00Z')
|
||||
END AS ts_to
|
||||
),
|
||||
slot_spine as (
|
||||
select gs as interval_start
|
||||
from bounds b,
|
||||
slot_spine AS (
|
||||
SELECT gs AS interval_start
|
||||
FROM bounds b,
|
||||
generate_series(
|
||||
b.ts_from,
|
||||
(b.ts_to - interval '15 minutes')::timestamptz,
|
||||
interval '15 minutes'
|
||||
) as gs
|
||||
) AS gs
|
||||
),
|
||||
fc as (
|
||||
select
|
||||
u.interval_start,
|
||||
coalesce(sum(u.power_w), 0)::bigint as pv_forecast_total_w
|
||||
from (
|
||||
select distinct on (fpi.interval_start, fpr.pv_array_id)
|
||||
fpi.interval_start,
|
||||
fpi.power_w
|
||||
from ems.forecast_pv_interval fpi
|
||||
join ems.forecast_pv_run fpr on fpr.id = fpi.run_id
|
||||
join ems.asset_pv_array apa
|
||||
on apa.id = fpr.pv_array_id
|
||||
and apa.site_id = fpr.site_id
|
||||
cross join bounds b
|
||||
where fpr.site_id = p_site_id
|
||||
and fpr.status = 'ok'
|
||||
and fpi.interval_start >= b.ts_from
|
||||
and fpi.interval_start < b.ts_to
|
||||
order by fpi.interval_start, fpr.pv_array_id, fpr.created_at desc
|
||||
) u
|
||||
group by u.interval_start
|
||||
fc_by_array AS (
|
||||
SELECT DISTINCT ON (fpi.interval_start, fpr.pv_array_id)
|
||||
fpi.interval_start,
|
||||
fpr.pv_array_id,
|
||||
fpi.power_w::bigint AS power_w
|
||||
FROM ems.forecast_pv_interval fpi
|
||||
JOIN ems.forecast_pv_run fpr ON fpr.id = fpi.run_id
|
||||
JOIN ems.asset_pv_array apa
|
||||
ON apa.id = fpr.pv_array_id
|
||||
AND apa.site_id = fpr.site_id
|
||||
CROSS JOIN bounds b
|
||||
WHERE fpr.site_id = p_site_id
|
||||
AND fpr.status = 'ok'
|
||||
AND fpi.interval_start >= b.ts_from
|
||||
AND fpi.interval_start < b.ts_to
|
||||
ORDER BY fpi.interval_start, fpr.pv_array_id, fpr.created_at DESC
|
||||
),
|
||||
profile as (
|
||||
select ems.fn_pv_forecast_delta_profile(
|
||||
fc_totals AS (
|
||||
SELECT u.interval_start, coalesce(sum(u.power_w), 0)::bigint AS pv_forecast_total_w
|
||||
FROM fc_by_array u
|
||||
GROUP BY u.interval_start
|
||||
),
|
||||
profile AS (
|
||||
SELECT ems.fn_pv_forecast_delta_profile(
|
||||
p_site_id,
|
||||
p_delta_data_from,
|
||||
p_delta_data_to,
|
||||
p_half_life_days,
|
||||
p_threshold_w
|
||||
) as j
|
||||
) AS j
|
||||
),
|
||||
deltas as (
|
||||
select
|
||||
(x->>'slot_of_day')::int as slot_of_day,
|
||||
(x->>'delta_w')::int as delta_w,
|
||||
(x->>'sample_count')::int as sample_count
|
||||
from profile p
|
||||
cross join lateral jsonb_array_elements(p.j->'deltas') as x
|
||||
)
|
||||
select coalesce(
|
||||
jsonb_agg(
|
||||
jsonb_build_object(
|
||||
'interval_start', s.interval_start,
|
||||
'pv_forecast_total_w', coalesce(fc.pv_forecast_total_w, 0),
|
||||
'pv_forecast_corrected_w',
|
||||
greatest(
|
||||
delta_by_array AS (
|
||||
SELECT (kv.key)::int AS pv_array_id,
|
||||
(x->>'slot_of_day')::int AS slot_of_day,
|
||||
(x->>'delta_w')::int AS delta_w
|
||||
FROM profile p
|
||||
CROSS JOIN LATERAL jsonb_each((p.j)->'deltas_by_array') kv(key, value)
|
||||
CROSS JOIN LATERAL jsonb_array_elements(kv.value->'deltas') x
|
||||
),
|
||||
deltas_legacy AS (
|
||||
SELECT (x->>'slot_of_day')::int AS slot_of_day,
|
||||
(x->>'delta_w')::int AS delta_w
|
||||
FROM profile p
|
||||
CROSS JOIN LATERAL jsonb_array_elements(p.j->'deltas') x
|
||||
),
|
||||
corrected AS (
|
||||
SELECT
|
||||
s.interval_start,
|
||||
coalesce(ft.pv_forecast_total_w, 0)::bigint AS pv_forecast_total_w,
|
||||
coalesce(
|
||||
CASE
|
||||
WHEN EXISTS (SELECT 1 FROM delta_by_array LIMIT 1) THEN (
|
||||
SELECT sum(greatest(0, fa.power_w - coalesce(d.delta_w, 0)))::bigint
|
||||
FROM fc_by_array fa
|
||||
CROSS JOIN tz
|
||||
LEFT JOIN delta_by_array d
|
||||
ON d.pv_array_id = fa.pv_array_id
|
||||
AND d.slot_of_day = (
|
||||
(
|
||||
(extract(hour FROM (s.interval_start AT TIME ZONE tz.tz_name))::int * 60)
|
||||
+ extract(minute FROM (s.interval_start AT TIME ZONE tz.tz_name))::int
|
||||
) / 15
|
||||
)
|
||||
WHERE fa.interval_start = s.interval_start
|
||||
)
|
||||
ELSE greatest(
|
||||
0,
|
||||
coalesce(fc.pv_forecast_total_w, 0)::int
|
||||
coalesce(ft.pv_forecast_total_w, 0)::bigint
|
||||
- coalesce(
|
||||
(
|
||||
select d.delta_w
|
||||
from deltas d
|
||||
cross join tz
|
||||
where d.slot_of_day = (
|
||||
SELECT d.delta_w
|
||||
FROM deltas_legacy d
|
||||
CROSS JOIN tz
|
||||
WHERE d.slot_of_day = (
|
||||
(
|
||||
(extract(hour from (s.interval_start at time zone tz.tz_name))::int * 60)
|
||||
+ extract(minute from (s.interval_start at time zone tz.tz_name))::int
|
||||
(extract(hour FROM (s.interval_start AT TIME ZONE tz.tz_name))::int * 60)
|
||||
+ extract(minute FROM (s.interval_start AT TIME ZONE tz.tz_name))::int
|
||||
) / 15
|
||||
)
|
||||
),
|
||||
0
|
||||
)
|
||||
),
|
||||
)
|
||||
END,
|
||||
0
|
||||
)::bigint AS pv_forecast_corrected_w
|
||||
FROM slot_spine s
|
||||
LEFT JOIN fc_totals ft ON ft.interval_start = s.interval_start
|
||||
)
|
||||
SELECT coalesce(
|
||||
jsonb_agg(
|
||||
jsonb_build_object(
|
||||
'interval_start', c.interval_start,
|
||||
'pv_forecast_total_w', c.pv_forecast_total_w,
|
||||
'pv_forecast_corrected_w', c.pv_forecast_corrected_w,
|
||||
'slot_of_day',
|
||||
(
|
||||
(
|
||||
(extract(hour from (s.interval_start at time zone tz.tz_name))::int * 60)
|
||||
+ extract(minute from (s.interval_start at time zone tz.tz_name))::int
|
||||
(extract(hour FROM (c.interval_start AT TIME ZONE tz.tz_name))::int * 60)
|
||||
+ extract(minute FROM (c.interval_start AT TIME ZONE tz.tz_name))::int
|
||||
) / 15
|
||||
)
|
||||
)
|
||||
order by s.interval_start
|
||||
ORDER BY c.interval_start
|
||||
),
|
||||
'[]'::jsonb
|
||||
)
|
||||
from slot_spine s
|
||||
cross join tz
|
||||
left join fc on fc.interval_start = s.interval_start;
|
||||
FROM corrected c
|
||||
CROSS JOIN tz;
|
||||
$fn$;
|
||||
|
||||
comment on function ems.fn_forecast_pv_slots_range_corrected is
|
||||
'JSON pole {interval_start, pv_forecast_total_w, pv_forecast_corrected_w, slot_of_day} po 15 min pro [p_from, p_to). Korekce je aditivní delta profil z fn_pv_forecast_delta_profile (parametry delty = defaulty v té funkci). Horizont je omezený na max. 60 dní.';
|
||||
COMMENT ON FUNCTION ems.fn_forecast_pv_slots_range_corrected IS
|
||||
'JSON pole {interval_start, pv_forecast_total_w, pv_forecast_corrected_w, slot_of_day} po 15 min pro [p_from, p_to). Korekce per pv_array_id z fn_pv_forecast_delta_profile.deltas_by_array (fallback na jedno pole `deltas`). Horizont max. 60 dní.';
|
||||
|
||||
@@ -28,6 +28,7 @@ GRANT SELECT ON ems.vw_operating_mode TO ems_anon;
|
||||
GRANT SELECT ON ems.vw_telemetry_hourly_7d TO ems_anon;
|
||||
GRANT SELECT ON ems.vw_telemetry_15m_7d TO ems_anon;
|
||||
GRANT SELECT ON ems.forecast_accuracy TO ems_anon;
|
||||
GRANT SELECT ON ems.site_pv_forecast_calibration TO ems_anon;
|
||||
GRANT SELECT ON ems.vw_forecast_accuracy_by_lead_time TO ems_anon;
|
||||
GRANT SELECT ON ems.vw_forecast_accuracy_daily TO ems_anon;
|
||||
GRANT SELECT ON ems.consumption_baseline_stats TO ems_anon;
|
||||
|
||||
Reference in New Issue
Block a user