2vzorky pro korekci predikce
This commit is contained in:
Dusan Vojacek
@@ -11,7 +11,7 @@ create or replace function ems.fn_pv_forecast_delta_profile(
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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 null,
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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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)
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@@ -227,4 +227,4 @@ as $fn$
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$fn$;
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comment on function ems.fn_pv_forecast_delta_profile is
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'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) * volitelně top_n_days (jen N nejlepších kalendářních dní podle w_energy*w_smooth, ostatní ztlumené) * power(day_weight, day_weight_gamma). Vrací JSON {deltas:[{slot_of_day, delta_w, sample_count}], ...}. Cutoff dat od 2026-04-12 Europe/Prague.';
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'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.';
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222
scripts/analysis/pv_delta_profile_diagnostics.sql
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222
scripts/analysis/pv_delta_profile_diagnostics.sql
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@@ -0,0 +1,222 @@
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-- Diagnostika: z kterých kalendářních dní (Europe/Prague) se skládá váha pro delta profil
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-- (stejná logika jako ems.fn_pv_forecast_delta_profile: best → slots → day_stats → day_rank → váhy w).
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--
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-- Uprav params (site_id, okno, half_life, threshold, top_n_days / non_top / gamma) a spusť v psql.
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-- Jedna řádka = jeden kalendářní den v okně; p_top_n_days mění tier u vah (ne počet řádků).
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WITH params AS (
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SELECT
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2::int AS site_id,
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(now() - interval '60 days')::timestamptz AS p_data_from,
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now()::timestamptz AS p_data_to,
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14::numeric AS half_life_days,
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150::int AS threshold_w,
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NULL::int AS p_top_n_days,
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0.02::numeric AS p_non_top_day_factor,
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1.0::numeric AS p_day_weight_gamma
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),
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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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JOIN params p ON s.id = p.site_id
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),
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cutoff AS (
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SELECT timestamptz '2026-04-11T22:00:00Z' AS min_ts
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),
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bounds AS (
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SELECT
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greatest(p.p_data_from, p.p_data_to - interval '120 days', (SELECT min_ts FROM cutoff)) AS ts_from,
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p.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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FROM params p
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),
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best AS (
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SELECT
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fa.interval_start,
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fa.pv_array_id,
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fa.forecast_power_w,
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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
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JOIN params p ON fa.site_id = p.site_id
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WHERE fa.interval_start >= b.ts_from
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AND fa.interval_start < b.ts_to
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AND fa.actual_power_w IS NOT NULL
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AND fa.forecast_created_at <= fa.interval_start
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),
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slots AS (
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SELECT
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b.interval_start,
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sum(b.forecast_power_w)::numeric AS forecast_total_w,
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sum(b.actual_power_w)::numeric AS actual_total_w,
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(
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(extract(hour FROM (b.interval_start AT TIME ZONE tz.tz_name))::int * 60)
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+ extract(minute FROM (b.interval_start AT TIME ZONE tz.tz_name))::int
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) / 15 AS slot_of_day,
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(b.interval_start AT TIME ZONE tz.tz_name)::date AS day_local,
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extract(epoch FROM (now() - b.interval_start)) / 86400.0 AS age_days
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FROM best b
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CROSS JOIN tz
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WHERE b.rn = 1
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GROUP BY b.interval_start, slot_of_day, day_local, tz.tz_name
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),
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day_energy AS (
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SELECT s.day_local, sum(s.actual_total_w)::numeric / 4000.0 AS energy_kwh
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FROM slots s
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GROUP BY s.day_local
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),
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ref AS (
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SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY de.energy_kwh) AS med_kwh
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FROM day_energy de
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),
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slot_steps AS (
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SELECT
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s.*,
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lag(s.actual_total_w) OVER (PARTITION BY s.day_local ORDER BY s.interval_start) AS prev_actual_w
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FROM slots s
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WHERE s.slot_of_day BETWEEN 20 AND 80
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AND s.actual_total_w > (SELECT threshold_w FROM bounds)
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),
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day_jump AS (
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SELECT
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ss.day_local,
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percentile_cont(0.5) WITHIN GROUP (ORDER BY abs(ss.actual_total_w - ss.prev_actual_w)) AS med_jump_w
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FROM slot_steps ss
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WHERE ss.prev_actual_w IS NOT NULL
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GROUP BY ss.day_local
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),
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day_med AS (
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SELECT
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s.day_local,
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percentile_cont(0.5) WITHIN GROUP (ORDER BY s.actual_total_w) AS p50_actual_w
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FROM slots s
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WHERE s.actual_total_w > (SELECT threshold_w FROM bounds)
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GROUP BY s.day_local
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),
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day_stats AS (
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SELECT
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de.day_local,
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de.energy_kwh,
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dj.med_jump_w,
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dm.p50_actual_w,
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CASE
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WHEN (SELECT med_kwh FROM ref) IS NULL OR (SELECT med_kwh FROM ref) <= 0 THEN 0.5
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ELSE greatest(
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0.0,
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least(
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1.0,
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(de.energy_kwh - (SELECT med_kwh FROM ref) * 0.55)
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/ nullif((SELECT med_kwh FROM ref) * 0.35, 0)
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)
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)
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END AS w_energy,
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CASE
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WHEN dj.med_jump_w IS NULL OR dm.p50_actual_w IS NULL THEN 0.35
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ELSE greatest(
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0.0,
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least(
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1.0,
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1.0
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- (
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dj.med_jump_w
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/ nullif(greatest(300.0, dm.p50_actual_w * 0.25), 0)
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)
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)
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)
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END AS w_smooth
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FROM day_energy de
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LEFT JOIN day_jump dj ON dj.day_local = de.day_local
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LEFT JOIN day_med dm ON dm.day_local = de.day_local
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),
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day_rank AS (
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SELECT
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ds.day_local,
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row_number() OVER (
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ORDER BY
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(coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35)) DESC,
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ds.day_local DESC
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) AS rn,
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(coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35)) AS day_score
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FROM day_stats ds
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),
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filtered AS (
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SELECT
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s.day_local,
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s.slot_of_day,
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exp(-s.age_days / nullif((SELECT half_life_days FROM bounds), 0))
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* (
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CASE
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WHEN (SELECT p_top_n_days FROM params) IS NULL THEN 1::numeric
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WHEN (SELECT p_top_n_days FROM params) < 1 THEN 1::numeric
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WHEN dr.rn <= (SELECT p_top_n_days FROM params) THEN 1::numeric
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ELSE greatest(
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0::numeric,
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least(1::numeric, coalesce((SELECT p_non_top_day_factor FROM params), 0.02))
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)
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END
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)
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* (
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0.05
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+ 0.95
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* power(
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greatest(
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0.0,
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least(1.0, coalesce(ds.w_energy, 0.35) * coalesce(ds.w_smooth, 0.35))
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),
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greatest(
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0.25,
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least(coalesce((SELECT p_day_weight_gamma FROM params), 1.0), 8.0)
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)
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)
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) AS w
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FROM slots s
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CROSS JOIN bounds b
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LEFT JOIN day_stats ds ON ds.day_local = s.day_local
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LEFT JOIN day_rank dr ON dr.day_local = s.day_local
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WHERE s.slot_of_day BETWEEN 0 AND 95
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AND (s.actual_total_w > b.threshold_w OR s.forecast_total_w > b.threshold_w)
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),
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by_day AS (
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SELECT day_local, sum(w) AS w_sum, count(*)::bigint AS slot_rows
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FROM filtered
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GROUP BY day_local
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),
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tot AS (
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SELECT sum(w_sum) AS w_tot FROM by_day
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)
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SELECT
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dr.rn AS day_rank,
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ds.day_local,
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round(ds.energy_kwh::numeric, 2) AS energy_kwh,
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round(ds.w_energy::numeric, 3) AS w_energy,
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round(ds.w_smooth::numeric, 3) AS w_smooth,
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round(dr.day_score::numeric, 4) AS day_score,
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round(coalesce(bd.w_sum, 0)::numeric, 2) AS sum_w_in_filtered,
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coalesce(bd.slot_rows, 0::bigint) AS slot_rows,
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round(
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(100.0 * coalesce(bd.w_sum, 0) / nullif((SELECT w_tot FROM tot), 0))::numeric,
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2
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) AS pct_of_total_weight
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FROM day_stats ds
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JOIN day_rank dr ON dr.day_local = ds.day_local
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LEFT JOIN by_day bd ON bd.day_local = ds.day_local
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ORDER BY dr.rn;
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-- Shrnutí okna + výstup funkce (stejné parametry jako v params výše):
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-- SELECT (ems.fn_pv_forecast_delta_profile(
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-- (SELECT site_id FROM params),
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-- (SELECT p_data_from FROM params),
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-- (SELECT p_data_to FROM params),
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-- (SELECT half_life_days FROM params),
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-- (SELECT threshold_w FROM params),
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-- (SELECT p_top_n_days FROM params),
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-- (SELECT p_non_top_day_factor FROM params),
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-- (SELECT p_day_weight_gamma FROM params)
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-- ))->'data_from' AS data_from,
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-- (ems.fn_pv_forecast_delta_profile(...))->'data_to' AS data_to;
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