Add Open Energy Dataset star schema (README, table docs, dim/fact Parquet tables)

README.md

@@ -0,0 +1,200 @@
+---
+type: Dataset
+title: Open Energy Dataset — Star Schema
+description: Star schema of 15-min industrial energy aggregates from 43 monitored assets over 12 months
+resource: .
+tags: [dataset, energy, industrial, star-schema, SCADA]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+source_paper: "Flynn et al., Data 2026, 11, 101"
+source_doi: "10.3390/data11050101"
+dataset_doi: "10.5281/zenodo.19180972"
+license: cc-by-4.0
+---
+
+# Open Energy Dataset — Star Schema
+
+## 1. Overview
+
+This star schema models the **Gold-layer** 15-minute energy aggregates from an industrial manufacturing facility in Ireland. The source dataset covers 43 monitored assets over ~12 months (2024-12-31 to 2025-12-31), with 1,039,873 ALL-phase windows totalling 2.96 GWh of measured electrical energy.
+
+The facility employs ~150 personnel under continuous production. Monitored loads include hydraulic presses, air compressors, AHUs, heat pumps, mixers, material handling, and utilities. Metering was deployed progressively (staged commissioning), so individual assets have different temporal coverage within the observation window.
+
+**Source:** Flynn et al., *Data* 2026, 11, 101 — [doi:10.3390/data11050101](https://doi.org/10.3390/data11050101)
+**Dataset:** [doi:10.5281/zenodo.19180972](https://doi.org/10.5281/zenodo.19180972)
+
+## 2. Tables
+
+| Table | Role | Rows | Description |
+|-------|------|-----:|-------------|
+| [fact_energy_15m](fact_energy_15m.md) | fact | 12,076,111 | Unpivoted 15-min energy measurements (3 signals x 4 phases) |
+| [dim_asset](dim_asset.md) | dimension | 43 | Monitored asset metadata (meter type, protocol, stream dates) |
+| [dim_signal_info](dim_signal_info.md) | dimension | 12 | Signal catalogue (metric name + phase + unit) |
+| [dim_observation_window](dim_observation_window.md) | dimension | 1,554 | Deduplicated per-window coverage and reliability profiles |
+| [fact_data_quality_event](fact_data_quality_event.md) | fact | 24 | Documented instrumentation fault events |
+
+### File inventory
+
+```
+result/
+├── README.md                              (this file)
+├── build_star_schema.py                   (ETL script)
+├── fact_energy_15m.md                     (table concept)
+├── dim_asset.md                           (table concept)
+├── dim_signal_info.md                     (table concept)
+├── dim_observation_window.md              (table concept)
+├── fact_data_quality_event.md             (table concept)
+├── dim/
+│   ├── dim_asset.parquet                  (43 rows, <1 KB)
+│   ├── dim_signal_info.parquet            (12 rows, <1 KB)
+│   └── dim_observation_window.parquet     (1,554 rows, 14 KB)
+└── fact/
+    ├── fact_energy_15m.parquet            (12,076,111 rows, 75.8 MB)
+    └── fact_data_quality_event.parquet    (24 rows, <1 KB)
+```
+
+All parquet files use ZSTD compression. The fact table is a single file (not partitioned).
+
+## 3. Schema Diagram
+
+```mermaid
+erDiagram
+    dim_asset {
+        VARCHAR asset_id PK
+        VARCHAR asset_type
+        VARCHAR em_manufacturer
+        VARCHAR em_model
+        VARCHAR em_communication_protocol
+        VARCHAR em_acquisition_pathway
+        TIMESTAMP stream_start_utc
+        TIMESTAMP stream_end_utc
+        BOOLEAN is_submeter
+    }
+
+    dim_signal_info {
+        INTEGER signal_id PK
+        VARCHAR signal_name
+        VARCHAR phase
+        VARCHAR unit
+        VARCHAR description
+    }
+
+    fact_energy_15m {
+        VARCHAR asset_id FK
+        INTEGER signal_id FK
+        TIMESTAMP time
+        DOUBLE value_float
+        INTEGER observation_window_id FK
+    }
+
+    dim_observation_window {
+        INTEGER observation_window_id PK
+        DOUBLE minutes
+        DOUBLE seconds_observed
+        DOUBLE data_coverage_pct
+        DOUBLE seconds_reliable
+        DOUBLE reliable_coverage_pct
+        INTEGER is_reliable_window
+    }
+
+    fact_data_quality_event {
+        INTEGER event_id PK
+        VARCHAR asset_id FK
+        VARCHAR issue_type
+        VARCHAR issue_detail
+        TIMESTAMP asset_stream_start
+        TIMESTAMP asset_stream_end
+        TIMESTAMP issue_start
+        TIMESTAMP issue_end
+        BOOLEAN issue_persisted_to_stream_end
+    }
+
+    dim_asset ||--o{ fact_energy_15m : "asset_id"
+    dim_asset ||--o{ fact_data_quality_event : "asset_id"
+    dim_signal_info ||--o{ fact_energy_15m : "signal_id"
+    dim_observation_window ||--o{ fact_energy_15m : "observation_window_id"
+```
+
+## 4. Design Principles
+
+- **Grain of `fact_energy_15m`:** one row per (`asset_id`, `signal_id`, `time`). The three source measures (energy, demand, avg power) and four phases (L1, L2, L3, ALL) are unpivoted into rows with a single `value_float` column.
+- **Signals as a dimension:** `dim_signal_info` encodes both the signal name and the electrical phase, enabling flexible filtering and pivoting without hard-coded column names.
+- **Coverage as a deduplicated dimension:** `dim_observation_window` holds a surrogate `observation_window_id` PK plus the six coverage/reliability value columns. Only 1,554 distinct profiles exist across 4.1M source rows, so each fact row carries a compact FK instead of duplicating coverage columns.
+- **Data quality events as a separate fact:** instrumentation faults are modelled as time-bounded events referencing the asset dimension, enabling temporal overlap analysis with energy facts.
+
+## 5. Source Mapping
+
+| Star Schema Table | Source File(s) | Transform |
+|-------------------|----------------|-----------|
+| `fact_energy_15m` | Gold-layer Parquet partitions (`data_parquet/asset_id=*/dt_utc=*/*.parquet`) | Unpivot `Energy_kWh_15m`, `Demand_kW`, `AvgPower_kW_15m` x `Phase` into rows; rename `window_start_utc` -> `time`; assign `signal_id` and `observation_window_id` FKs |
+| `dim_asset` | `metadata/AssetList.csv` | Direct load |
+| `dim_signal_info` | Static catalogue (12 rows) | Generated from the 3 signal names x 4 phases |
+| `dim_observation_window` | Gold-layer Parquet partitions | DISTINCT on 6 coverage columns; assign surrogate `observation_window_id` |
+| `fact_data_quality_event` | `metadata/meter_data_quality_log.csv` | Direct load with surrogate `event_id` |
+
+## 6. Typical Questions
+
+Questions an analyst might ask of this dataset, with expected answers derived from the published validation summaries.
+
+### Energy totals
+
+**Q: What is the total measured energy across all assets (ALL phase)?**
+A: 2,958,571 kWh (2.96 GWh). This is the sum of `energy_kwh_15m` for the ALL phase across all assets and windows.
+
+**Q: How much energy did the grid connection (mi_a) import over the observation period?**
+A: 1,228,639 kWh. This is the single largest energy contributor in the dataset.
+
+**Q: Which asset type consumes the most energy?**
+A: Electrical_GridImport (1,228,639 kWh via mi_a), but among consumer loads: HVAC_AirExtraction (206,329 kWh, driven by ex_b at 191,771 kWh), followed by Press_Main (525,161 kWh across 9 presses) and CompressedAir (144,771 kWh from 2 compressors).
+
+**Q: What is the total energy measured across all rows including per-phase breakdowns?**
+A: 5,917,142 kWh across all 4,103,703 rows (L1 + L2 + L3 + ALL phases combined).
+
+### Temporal coverage
+
+**Q: What is the time span of the dataset?**
+A: 2024-12-31 07:30 UTC to 2025-12-31 23:45 UTC (approximately 12 months).
+
+**Q: How many 15-minute windows are in the ALL-phase dataset?**
+A: 1,039,873 windows across 43 assets.
+
+**Q: Which month had the highest energy consumption?**
+A: October 2025 with 418,908 kWh (36-37 active assets), followed by November 2025 at 403,469 kWh (40 active assets).
+
+**Q: How did the number of active assets change over time?**
+A: From 14 assets in January 2025 to 43 assets in December 2025, reflecting staged commissioning. The jump from 14 to 18 occurred in February, then 22 in March, 26 in April, and 36 in May when supply/distribution meters came online.
+
+### Data quality
+
+**Q: What is the mean data coverage across the dataset?**
+A: 99.99% mean data coverage (ALL phase). Median is 100.00%.
+
+**Q: What percentage of ALL-phase windows are reliable?**
+A: 97.72% (1,016,182 out of 1,039,873 windows satisfy IsReliableWindow = 1).
+
+**Q: Which asset has the lowest reliable window percentage?**
+A: p_f (Press_IntegratedAutomationCell) at 70.64%, followed by sb_c (Electrical_Distribution) at 63.40%. Both have documented instrumentation issues.
+
+**Q: How many data quality events are there and what types?**
+A: 24 events across 18 assets. Types include reversed CT polarity (most common), phase reference misalignment (single/multi/all phases), tag misconfiguration, and CT ratio misconfiguration.
+
+**Q: Which assets have unresolved faults (persisted to stream end)?**
+A: ahu_a (L3 tag misconfiguration) and mix_b (L3 tag misconfiguration) — both had SCADA tag issues that were never corrected during the observation period.
+
+### Asset comparisons
+
+**Q: How many assets use Modbus RTU vs Modbus TCP/IP?**
+A: 4 assets use Modbus RTU (hp_a, hp_b, sb_c, mi_b — all Rayleigh meters via gateway). The remaining 39 use Modbus TCP/IP (Weidmuller EM220 meters).
+
+**Q: Which individual press consumes the most energy?**
+A: p_k (Press Main and Robot) at 207,445 kWh, followed by p_f (Press IntegratedAutomationCell) at 111,980 kWh and p_b (Press Main) at 88,014 kWh.
+
+**Q: What is the energy consumption of the two air compressors combined?**
+A: 144,771 kWh (comp_a: 68,392 kWh, comp_b: 76,379 kWh).
+
+### Load profiles
+
+**Q: Do the heat pumps consume significant energy?**
+A: No. hp_a consumed 69 kWh and hp_b consumed 12 kWh over the observation period — negligible compared to other loads.
+
+**Q: What fraction of total grid import is captured by sub-metered assets?**
+A: The dataset is not a closed energy system — partial sub-metering means summing all consumer meters will not equal grid import (mi_a). This is a documented limitation of the retrofit monitoring deployment.

description.md

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+---
+type: Dataset
+title: Open Energy Dataset — Star Schema
+description: Star schema of 15-min industrial energy aggregates from 43 monitored assets over 12 months
+resource: .
+tags: [dataset, energy, industrial, star-schema, SCADA]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+source_paper: "Flynn et al., Data 2026, 11, 101"
+source_doi: "10.3390/data11050101"
+dataset_doi: "10.5281/zenodo.19180972"
+license: CC BY 4.0
+---
+
+# Open Energy Dataset — Star Schema
+
+## 1. Overview
+
+This star schema models the **Gold-layer** 15-minute energy aggregates from an industrial manufacturing facility in Ireland. The source dataset covers 43 monitored assets over ~12 months (2024-12-31 to 2025-12-31), with 1,039,873 ALL-phase windows totalling 2.96 GWh of measured electrical energy.
+
+The facility employs ~150 personnel under continuous production. Monitored loads include hydraulic presses, air compressors, AHUs, heat pumps, mixers, material handling, and utilities. Metering was deployed progressively (staged commissioning), so individual assets have different temporal coverage within the observation window.
+
+**Source:** Flynn et al., *Data* 2026, 11, 101 — [doi:10.3390/data11050101](https://doi.org/10.3390/data11050101)
+**Dataset:** [doi:10.5281/zenodo.19180972](https://doi.org/10.5281/zenodo.19180972)
+
+## 2. Tables
+
+| Table | Role | Rows | Description |
+|-------|------|-----:|-------------|
+| [fact_energy_15m](fact_energy_15m.md) | fact | 12,076,111 | Unpivoted 15-min energy measurements (3 signals x 4 phases) |
+| [dim_asset](dim_asset.md) | dimension | 43 | Monitored asset metadata (meter type, protocol, stream dates) |
+| [dim_signal_info](dim_signal_info.md) | dimension | 12 | Signal catalogue (metric name + phase + unit) |
+| [dim_observation_window](dim_observation_window.md) | dimension | 1,554 | Deduplicated per-window coverage and reliability profiles |
+| [fact_data_quality_event](fact_data_quality_event.md) | fact | 24 | Documented instrumentation fault events |
+
+### File inventory
+
+```
+result/
+├── README.md                              (this file)
+├── build_star_schema.py                   (ETL script)
+├── fact_energy_15m.md                     (table concept)
+├── dim_asset.md                           (table concept)
+├── dim_signal_info.md                     (table concept)
+├── dim_observation_window.md              (table concept)
+├── fact_data_quality_event.md             (table concept)
+├── dim/
+│   ├── dim_asset.parquet                  (43 rows, <1 KB)
+│   ├── dim_signal_info.parquet            (12 rows, <1 KB)
+│   └── dim_observation_window.parquet     (1,554 rows, 14 KB)
+└── fact/
+    ├── fact_energy_15m.parquet            (12,076,111 rows, 75.8 MB)
+    └── fact_data_quality_event.parquet    (24 rows, <1 KB)
+```
+
+All parquet files use ZSTD compression. The fact table is a single file (not partitioned).
+
+## 3. Schema Diagram
+
+```mermaid
+erDiagram
+    dim_asset {
+        VARCHAR asset_id PK
+        VARCHAR asset_type
+        VARCHAR em_manufacturer
+        VARCHAR em_model
+        VARCHAR em_communication_protocol
+        VARCHAR em_acquisition_pathway
+        TIMESTAMP stream_start_utc
+        TIMESTAMP stream_end_utc
+        BOOLEAN is_submeter
+    }
+
+    dim_signal_info {
+        INTEGER signal_id PK
+        VARCHAR signal_name
+        VARCHAR phase
+        VARCHAR unit
+        VARCHAR description
+    }
+
+    fact_energy_15m {
+        VARCHAR asset_id FK
+        INTEGER signal_id FK
+        TIMESTAMP time
+        DOUBLE value_float
+        INTEGER observation_window_id FK
+    }
+
+    dim_observation_window {
+        INTEGER observation_window_id PK
+        DOUBLE minutes
+        DOUBLE seconds_observed
+        DOUBLE data_coverage_pct
+        DOUBLE seconds_reliable
+        DOUBLE reliable_coverage_pct
+        INTEGER is_reliable_window
+    }
+
+    fact_data_quality_event {
+        INTEGER event_id PK
+        VARCHAR asset_id FK
+        VARCHAR issue_type
+        VARCHAR issue_detail
+        TIMESTAMP asset_stream_start
+        TIMESTAMP asset_stream_end
+        TIMESTAMP issue_start
+        TIMESTAMP issue_end
+        BOOLEAN issue_persisted_to_stream_end
+    }
+
+    dim_asset ||--o{ fact_energy_15m : "asset_id"
+    dim_asset ||--o{ fact_data_quality_event : "asset_id"
+    dim_signal_info ||--o{ fact_energy_15m : "signal_id"
+    dim_observation_window ||--o{ fact_energy_15m : "observation_window_id"
+```
+
+## 4. Design Principles
+
+- **Grain of `fact_energy_15m`:** one row per (`asset_id`, `signal_id`, `time`). The three source measures (energy, demand, avg power) and four phases (L1, L2, L3, ALL) are unpivoted into rows with a single `value_float` column.
+- **Signals as a dimension:** `dim_signal_info` encodes both the signal name and the electrical phase, enabling flexible filtering and pivoting without hard-coded column names.
+- **Coverage as a deduplicated dimension:** `dim_observation_window` holds a surrogate `observation_window_id` PK plus the six coverage/reliability value columns. Only 1,554 distinct profiles exist across 4.1M source rows, so each fact row carries a compact FK instead of duplicating coverage columns.
+- **Data quality events as a separate fact:** instrumentation faults are modelled as time-bounded events referencing the asset dimension, enabling temporal overlap analysis with energy facts.
+
+## 5. Source Mapping
+
+| Star Schema Table | Source File(s) | Transform |
+|-------------------|----------------|-----------|
+| `fact_energy_15m` | Gold-layer Parquet partitions (`data_parquet/asset_id=*/dt_utc=*/*.parquet`) | Unpivot `Energy_kWh_15m`, `Demand_kW`, `AvgPower_kW_15m` x `Phase` into rows; rename `window_start_utc` -> `time`; assign `signal_id` and `observation_window_id` FKs |
+| `dim_asset` | `metadata/AssetList.csv` | Direct load |
+| `dim_signal_info` | Static catalogue (12 rows) | Generated from the 3 signal names x 4 phases |
+| `dim_observation_window` | Gold-layer Parquet partitions | DISTINCT on 6 coverage columns; assign surrogate `observation_window_id` |
+| `fact_data_quality_event` | `metadata/meter_data_quality_log.csv` | Direct load with surrogate `event_id` |
+
+## 6. Typical Questions
+
+Questions an analyst might ask of this dataset, with expected answers derived from the published validation summaries.
+
+### Energy totals
+
+**Q: What is the total measured energy across all assets (ALL phase)?**
+A: 2,958,571 kWh (2.96 GWh). This is the sum of `energy_kwh_15m` for the ALL phase across all assets and windows.
+
+**Q: How much energy did the grid connection (mi_a) import over the observation period?**
+A: 1,228,639 kWh. This is the single largest energy contributor in the dataset.
+
+**Q: Which asset type consumes the most energy?**
+A: Electrical_GridImport (1,228,639 kWh via mi_a), but among consumer loads: HVAC_AirExtraction (206,329 kWh, driven by ex_b at 191,771 kWh), followed by Press_Main (525,161 kWh across 9 presses) and CompressedAir (144,771 kWh from 2 compressors).
+
+**Q: What is the total energy measured across all rows including per-phase breakdowns?**
+A: 5,917,142 kWh across all 4,103,703 rows (L1 + L2 + L3 + ALL phases combined).
+
+### Temporal coverage
+
+**Q: What is the time span of the dataset?**
+A: 2024-12-31 07:30 UTC to 2025-12-31 23:45 UTC (approximately 12 months).
+
+**Q: How many 15-minute windows are in the ALL-phase dataset?**
+A: 1,039,873 windows across 43 assets.
+
+**Q: Which month had the highest energy consumption?**
+A: October 2025 with 418,908 kWh (36-37 active assets), followed by November 2025 at 403,469 kWh (40 active assets).
+
+**Q: How did the number of active assets change over time?**
+A: From 14 assets in January 2025 to 43 assets in December 2025, reflecting staged commissioning. The jump from 14 to 18 occurred in February, then 22 in March, 26 in April, and 36 in May when supply/distribution meters came online.
+
+### Data quality
+
+**Q: What is the mean data coverage across the dataset?**
+A: 99.99% mean data coverage (ALL phase). Median is 100.00%.
+
+**Q: What percentage of ALL-phase windows are reliable?**
+A: 97.72% (1,016,182 out of 1,039,873 windows satisfy IsReliableWindow = 1).
+
+**Q: Which asset has the lowest reliable window percentage?**
+A: p_f (Press_IntegratedAutomationCell) at 70.64%, followed by sb_c (Electrical_Distribution) at 63.40%. Both have documented instrumentation issues.
+
+**Q: How many data quality events are there and what types?**
+A: 24 events across 18 assets. Types include reversed CT polarity (most common), phase reference misalignment (single/multi/all phases), tag misconfiguration, and CT ratio misconfiguration.
+
+**Q: Which assets have unresolved faults (persisted to stream end)?**
+A: ahu_a (L3 tag misconfiguration) and mix_b (L3 tag misconfiguration) — both had SCADA tag issues that were never corrected during the observation period.
+
+### Asset comparisons
+
+**Q: How many assets use Modbus RTU vs Modbus TCP/IP?**
+A: 4 assets use Modbus RTU (hp_a, hp_b, sb_c, mi_b — all Rayleigh meters via gateway). The remaining 39 use Modbus TCP/IP (Weidmuller EM220 meters).
+
+**Q: Which individual press consumes the most energy?**
+A: p_k (Press Main and Robot) at 207,445 kWh, followed by p_f (Press IntegratedAutomationCell) at 111,980 kWh and p_b (Press Main) at 88,014 kWh.
+
+**Q: What is the energy consumption of the two air compressors combined?**
+A: 144,771 kWh (comp_a: 68,392 kWh, comp_b: 76,379 kWh).
+
+### Load profiles
+
+**Q: Do the heat pumps consume significant energy?**
+A: No. hp_a consumed 69 kWh and hp_b consumed 12 kWh over the observation period — negligible compared to other loads.
+
+**Q: What fraction of total grid import is captured by sub-metered assets?**
+A: The dataset is not a closed energy system — partial sub-metering means summing all consumer meters will not equal grid import (mi_a). This is a documented limitation of the retrofit monitoring deployment.
+
+---
+
+---
+type: Parquet
+title: dim_asset
+description: Monitored asset metadata including meter type, communication protocol, and stream dates
+resource: ./dim/dim_asset.parquet
+tags: [dimension, asset, metadata]
+timestamp: 2025-01-01T00:00:00Z/2025-12-31T23:59:00Z
+---
+
+# dim_asset
+
+One row per monitored asset. Source: `AssetList.csv`.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `asset_id` | VARCHAR | PK. Anonymised asset identifier (e.g. `ahu_a`, `p_b`, `mi_a`) |
+| `asset_type` | VARCHAR | Asset classification (e.g. `HVAC_AHU`, `Press_Main`, `Electrical_GridImport`) |
+| `em_manufacturer` | VARCHAR | Energy meter manufacturer (`Weidmuller`, `Rayleigh`) |
+| `em_model` | VARCHAR | Meter model (`EM220`, `RI-F200`, `RI-F400`) |
+| `em_communication_protocol` | VARCHAR | Communication protocol (`modbus_tcp_ip`, `modbus_rtu`) |
+| `em_acquisition_pathway` | VARCHAR | SCADA acquisition route (`direct_modbus_tcp`, `gateway_modbus_tcp_to_plc`, `gateway_modbus_rtu_to_plc`) |
+| `stream_start_utc` | TIMESTAMP | First observation timestamp for this asset |
+| `stream_end_utc` | TIMESTAMP | Last observation timestamp for this asset |
+| `is_submeter` | BOOLEAN | `TRUE` for downstream consumer/distribution meters; `FALSE` for supply-level meters (`mi_a`, `mi_b`) |
+
+**Row count:** 43 assets
+
+**Asset type distribution:**
+
+| Asset Type | Count |
+|------------|-------|
+| Press_Main | 9 |
+| Utilities | 5 |
+| ProcessMixing | 4 |
+| Press_Drive | 4 |
+| HVAC_AHU | 3 |
+| Electrical_Distribution | 3 |
+| Automation_Robot | 3 |
+| CompressedAir | 2 |
+| HVAC_AirExtraction | 2 |
+| HVAC_HeatPump | 2 |
+| Press_Heating | 2 |
+| Automation_Handling | 1 |
+| Electrical_GridImport | 1 |
+| Electrical_PVGeneration | 1 |
+| Press_IntegratedAutomationCell | 1 |
+
+---
+
+---
+type: Parquet
+title: dim_observation_window
+description: Deduplicated per-window data coverage and reliability profiles
+resource: ./dim/dim_observation_window.parquet
+tags: [dimension, coverage, reliability, data-quality]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+---
+
+# dim_observation_window
+
+Deduplicated coverage and reliability profiles. Each unique combination of coverage metrics gets a surrogate key (`observation_window_id`) referenced by [fact_energy_15m](fact_energy_15m.md).
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `observation_window_id` | INTEGER | PK. Surrogate key |
+| `minutes` | DOUBLE | Minutes of observed data in window (<= 15.0) |
+| `seconds_observed` | DOUBLE | Total seconds with telemetry coverage (capped at 900) |
+| `data_coverage_pct` | DOUBLE | Observed coverage as percentage: 100 x `seconds_observed` / 900 |
+| `seconds_reliable` | DOUBLE | Seconds from Silver rows passing all reliability checks (capped at 900) |
+| `reliable_coverage_pct` | DOUBLE | Reliable coverage as percentage: 100 x `seconds_reliable` / 900 |
+| `is_reliable_window` | INTEGER | Binary flag: `1` if `seconds_reliable` >= 720 (80% of 900 s), else `0` |
+
+**Row count:** 1,554 (deduplicated from 4,103,703 source rows)
+
+---
+
+---
+type: Parquet
+title: dim_signal_info
+description: Signal catalogue mapping signal_id to metric name, electrical phase, and unit
+resource: ./dim/dim_signal_info.parquet
+tags: [dimension, signal, catalogue]
+timestamp: 2026-07-22T00:00:00Z
+---
+
+# dim_signal_info
+
+One row per (signal name, phase) combination. Encodes what each `value_float` in [fact_energy_15m](fact_energy_15m.md) represents.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `signal_id` | INTEGER | PK. Surrogate key |
+| `signal_name` | VARCHAR | Metric name: `energy_kwh_15m`, `demand_kw`, `avg_power_kw_15m` |
+| `phase` | VARCHAR | Electrical phase: `L1`, `L2`, `L3`, or `ALL` (synthetic total) |
+| `unit` | VARCHAR | Physical unit of the measurement value |
+| `description` | VARCHAR | Human-readable description of the signal |
+
+**Row count:** 12 (3 signals x 4 phases)
+
+**Signal catalogue:**
+
+| signal_id | signal_name | phase | unit | description |
+|-----------|-------------|-------|------|-------------|
+| 1 | `energy_kwh_15m` | L1 | kWh | Energy consumed during window (phase L1) |
+| 2 | `energy_kwh_15m` | L2 | kWh | Energy consumed during window (phase L2) |
+| 3 | `energy_kwh_15m` | L3 | kWh | Energy consumed during window (phase L3) |
+| 4 | `energy_kwh_15m` | ALL | kWh | Energy consumed during window (all phases) |
+| 5 | `demand_kw` | L1 | kW | Average demand over window (phase L1) |
+| 6 | `demand_kw` | L2 | kW | Average demand over window (phase L2) |
+| 7 | `demand_kw` | L3 | kW | Average demand over window (phase L3) |
+| 8 | `demand_kw` | ALL | kW | Average demand over window (all phases) |
+| 9 | `avg_power_kw_15m` | L1 | kW | Reliability-weighted mean power (phase L1) |
+| 10 | `avg_power_kw_15m` | L2 | kW | Reliability-weighted mean power (phase L2) |
+| 11 | `avg_power_kw_15m` | L3 | kW | Reliability-weighted mean power (phase L3) |
+| 12 | `avg_power_kw_15m` | ALL | kW | Reliability-weighted mean power (all phases) |
+
+---
+
+---
+type: Parquet
+title: fact_data_quality_event
+description: Documented instrumentation fault events with time-bounded issue periods per asset
+resource: ./fact_data_quality_event.parquet
+tags: [fact, data-quality, instrumentation, faults]
+timestamp: 2025-01-01T00:00:00Z/2025-12-31T23:59:00Z
+---
+
+# fact_data_quality_event
+
+One row per documented instrumentation issue. Source: `meter_data_quality_log.csv`. These are time-bounded events recording known configuration or hardware faults that affect measurement validity.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `event_id` | INTEGER | PK. Surrogate key (auto-increment) |
+| `asset_id` | VARCHAR | FK -> [dim_asset](dim_asset.md). Affected asset |
+| `issue_type` | VARCHAR | Fault category (see taxonomy below) |
+| `issue_detail` | VARCHAR | Free-text description of the specific fault |
+| `asset_stream_start` | TIMESTAMP | Start of the asset's monitoring stream |
+| `asset_stream_end` | TIMESTAMP | End of the asset's monitoring stream |
+| `issue_start` | TIMESTAMP | When the issue began affecting measurements |
+| `issue_end` | TIMESTAMP | When the issue was rectified |
+| `issue_persisted_to_stream_end` | BOOLEAN | `TRUE` if the fault was never resolved within the observation period |
+
+**Row count:** 24 documented events across 18 assets
+
+**Issue type taxonomy:**
+
+| Issue Type | Category | Impact |
+|------------|----------|--------|
+| `reversed_ct_polarity` | Magnitude-affecting | Inverts sign on affected phase; invalidates power/energy magnitude |
+| `ct_ratio_misconfiguration` | Magnitude-affecting | Scales measurements by wrong CT ratio |
+| `tag_misconfiguration` | Magnitude-affecting | SCADA tag points to wrong register; reported values are incorrect |
+| `phase_reference_misalignment*` | Magnitude-affecting | Voltage references misaligned across phases; distorts per-phase readings |
+
+**Usage:** Overlay these events on energy time series to identify and exclude periods of known measurement invalidity. Join on `asset_id` and filter [fact_energy_15m](fact_energy_15m.md) rows where `time BETWEEN issue_start AND issue_end`.
+
+---
+
+---
+type: Parquet
+title: fact_energy_15m
+description: Unpivoted 15-min energy measurements (3 signals x 4 phases per asset window)
+resource: ./fact/fact_energy_15m.parquet
+tags: [fact, energy, timeseries, 15min]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+---
+
+# fact_energy_15m
+
+Grain: one row per asset, signal, and 15-minute UTC window. The three source measures (`Energy_kWh_15m`, `Demand_kW`, `AvgPower_kW_15m`) x four phases (`L1`, `L2`, `L3`, `ALL`) are unpivoted so that each combination becomes a separate row with a single `value_float`.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `asset_id` | VARCHAR | FK -> [dim_asset](dim_asset.md). Anonymised asset identifier |
+| `signal_id` | INTEGER | FK -> [dim_signal_info](dim_signal_info.md). Identifies the signal (metric + phase) |
+| `time` | TIMESTAMP | Start of 15-min aggregation window (UTC) |
+| `value_float` | DOUBLE | Measured value. Unit and semantics determined by `signal_id` |
+| `observation_window_id` | INTEGER | FK -> [dim_observation_window](dim_observation_window.md). Coverage profile for this window |
+
+**Composite key:** (`asset_id`, `signal_id`, `time`)
+
+**Row count:** 12,076,111
+
+**Notes:**
+- Active power is magnitude-only (clipped to >= 0) for consumer assets; signed for supply meters (`mi_a`, `mi_b`).
+- Energy integration uses a forward-hold (piecewise-constant) model; no interpolation is applied.
+- The `ALL` phase is a synthetic aggregate summing L1 + L2 + L3.
+- To pivot back to wide format, join [dim_signal_info](dim_signal_info.md) and pivot on `signal_name` x `phase`.

dim/dim_asset.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0474d422ad117b39b251ac81165562529e67f70f31156e9ccf79d8436293574
+size 2623

dim/dim_observation_window.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2cdab0ed3a3aebfa4a6ad871ed0fa824b08bdcc8659420f4d88a21aacd288b23
+size 14006

dim/dim_signal_info.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f7ac757c2c1c50a0295eaea831d8c9cdfa8d41e212d1dacd49d13bb04b8a8144
+size 1234

dim_asset.md

@@ -0,0 +1,46 @@
+---
+type: Parquet
+title: dim_asset
+description: Monitored asset metadata including meter type, communication protocol, and stream dates
+resource: ./dim/dim_asset.parquet
+tags: [dimension, asset, metadata]
+timestamp: 2025-01-01T00:00:00Z/2025-12-31T23:59:00Z
+---
+
+# dim_asset
+
+One row per monitored asset. Source: `AssetList.csv`.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `asset_id` | VARCHAR | PK. Anonymised asset identifier (e.g. `ahu_a`, `p_b`, `mi_a`) |
+| `asset_type` | VARCHAR | Asset classification (e.g. `HVAC_AHU`, `Press_Main`, `Electrical_GridImport`) |
+| `em_manufacturer` | VARCHAR | Energy meter manufacturer (`Weidmuller`, `Rayleigh`) |
+| `em_model` | VARCHAR | Meter model (`EM220`, `RI-F200`, `RI-F400`) |
+| `em_communication_protocol` | VARCHAR | Communication protocol (`modbus_tcp_ip`, `modbus_rtu`) |
+| `em_acquisition_pathway` | VARCHAR | SCADA acquisition route (`direct_modbus_tcp`, `gateway_modbus_tcp_to_plc`, `gateway_modbus_rtu_to_plc`) |
+| `stream_start_utc` | TIMESTAMP | First observation timestamp for this asset |
+| `stream_end_utc` | TIMESTAMP | Last observation timestamp for this asset |
+| `is_submeter` | BOOLEAN | `TRUE` for downstream consumer/distribution meters; `FALSE` for supply-level meters (`mi_a`, `mi_b`) |
+
+**Row count:** 43 assets
+
+**Asset type distribution:**
+
+| Asset Type | Count |
+|------------|-------|
+| Press_Main | 9 |
+| Utilities | 5 |
+| ProcessMixing | 4 |
+| Press_Drive | 4 |
+| HVAC_AHU | 3 |
+| Electrical_Distribution | 3 |
+| Automation_Robot | 3 |
+| CompressedAir | 2 |
+| HVAC_AirExtraction | 2 |
+| HVAC_HeatPump | 2 |
+| Press_Heating | 2 |
+| Automation_Handling | 1 |
+| Electrical_GridImport | 1 |
+| Electrical_PVGeneration | 1 |
+| Press_IntegratedAutomationCell | 1 |

dim_observation_window.md

@@ -0,0 +1,24 @@
+---
+type: Parquet
+title: dim_observation_window
+description: Deduplicated per-window data coverage and reliability profiles
+resource: ./dim/dim_observation_window.parquet
+tags: [dimension, coverage, reliability, data-quality]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+---
+
+# dim_observation_window
+
+Deduplicated coverage and reliability profiles. Each unique combination of coverage metrics gets a surrogate key (`observation_window_id`) referenced by [fact_energy_15m](fact_energy_15m.md).
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `observation_window_id` | INTEGER | PK. Surrogate key |
+| `minutes` | DOUBLE | Minutes of observed data in window (<= 15.0) |
+| `seconds_observed` | DOUBLE | Total seconds with telemetry coverage (capped at 900) |
+| `data_coverage_pct` | DOUBLE | Observed coverage as percentage: 100 x `seconds_observed` / 900 |
+| `seconds_reliable` | DOUBLE | Seconds from Silver rows passing all reliability checks (capped at 900) |
+| `reliable_coverage_pct` | DOUBLE | Reliable coverage as percentage: 100 x `seconds_reliable` / 900 |
+| `is_reliable_window` | INTEGER | Binary flag: `1` if `seconds_reliable` >= 720 (80% of 900 s), else `0` |
+
+**Row count:** 1,554 (deduplicated from 4,103,703 source rows)

dim_signal_info.md

@@ -0,0 +1,39 @@
+---
+type: Parquet
+title: dim_signal_info
+description: Signal catalogue mapping signal_id to metric name, electrical phase, and unit
+resource: ./dim/dim_signal_info.parquet
+tags: [dimension, signal, catalogue]
+timestamp: 2026-07-22T00:00:00Z
+---
+
+# dim_signal_info
+
+One row per (signal name, phase) combination. Encodes what each `value_float` in [fact_energy_15m](fact_energy_15m.md) represents.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `signal_id` | INTEGER | PK. Surrogate key |
+| `signal_name` | VARCHAR | Metric name: `energy_kwh_15m`, `demand_kw`, `avg_power_kw_15m` |
+| `phase` | VARCHAR | Electrical phase: `L1`, `L2`, `L3`, or `ALL` (synthetic total) |
+| `unit` | VARCHAR | Physical unit of the measurement value |
+| `description` | VARCHAR | Human-readable description of the signal |
+
+**Row count:** 12 (3 signals x 4 phases)
+
+**Signal catalogue:**
+
+| signal_id | signal_name | phase | unit | description |
+|-----------|-------------|-------|------|-------------|
+| 1 | `energy_kwh_15m` | L1 | kWh | Energy consumed during window (phase L1) |
+| 2 | `energy_kwh_15m` | L2 | kWh | Energy consumed during window (phase L2) |
+| 3 | `energy_kwh_15m` | L3 | kWh | Energy consumed during window (phase L3) |
+| 4 | `energy_kwh_15m` | ALL | kWh | Energy consumed during window (all phases) |
+| 5 | `demand_kw` | L1 | kW | Average demand over window (phase L1) |
+| 6 | `demand_kw` | L2 | kW | Average demand over window (phase L2) |
+| 7 | `demand_kw` | L3 | kW | Average demand over window (phase L3) |
+| 8 | `demand_kw` | ALL | kW | Average demand over window (all phases) |
+| 9 | `avg_power_kw_15m` | L1 | kW | Reliability-weighted mean power (phase L1) |
+| 10 | `avg_power_kw_15m` | L2 | kW | Reliability-weighted mean power (phase L2) |
+| 11 | `avg_power_kw_15m` | L3 | kW | Reliability-weighted mean power (phase L3) |
+| 12 | `avg_power_kw_15m` | ALL | kW | Reliability-weighted mean power (all phases) |

fact/fact_data_quality_event.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:942da42a7d46efcb622bd39a33e6d3e4eeb116c06e548adc18294e51b31097ec
+size 2444

fact/fact_energy_15m.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4946cd9909cbce5d215e261ce27a702b131b6337dfac69bae7ee9ff4148d19a
+size 79461031

fact_data_quality_event.md

@@ -0,0 +1,37 @@
+---
+type: Parquet
+title: fact_data_quality_event
+description: Documented instrumentation fault events with time-bounded issue periods per asset
+resource: ./fact/fact_data_quality_event.parquet
+tags: [fact, data-quality, instrumentation, faults]
+timestamp: 2025-01-01T00:00:00Z/2025-12-31T23:59:00Z
+---
+
+# fact_data_quality_event
+
+One row per documented instrumentation issue. Source: `meter_data_quality_log.csv`. These are time-bounded events recording known configuration or hardware faults that affect measurement validity.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `event_id` | INTEGER | PK. Surrogate key (auto-increment) |
+| `asset_id` | VARCHAR | FK -> [dim_asset](dim_asset.md). Affected asset |
+| `issue_type` | VARCHAR | Fault category (see taxonomy below) |
+| `issue_detail` | VARCHAR | Free-text description of the specific fault |
+| `asset_stream_start` | TIMESTAMP | Start of the asset's monitoring stream |
+| `asset_stream_end` | TIMESTAMP | End of the asset's monitoring stream |
+| `issue_start` | TIMESTAMP | When the issue began affecting measurements |
+| `issue_end` | TIMESTAMP | When the issue was rectified |
+| `issue_persisted_to_stream_end` | BOOLEAN | `TRUE` if the fault was never resolved within the observation period |
+
+**Row count:** 24 documented events across 18 assets
+
+**Issue type taxonomy:**
+
+| Issue Type | Category | Impact |
+|------------|----------|--------|
+| `reversed_ct_polarity` | Magnitude-affecting | Inverts sign on affected phase; invalidates power/energy magnitude |
+| `ct_ratio_misconfiguration` | Magnitude-affecting | Scales measurements by wrong CT ratio |
+| `tag_misconfiguration` | Magnitude-affecting | SCADA tag points to wrong register; reported values are incorrect |
+| `phase_reference_misalignment*` | Magnitude-affecting | Voltage references misaligned across phases; distorts per-phase readings |
+
+**Usage:** Overlay these events on energy time series to identify and exclude periods of known measurement invalidity. Join on `asset_id` and filter [fact_energy_15m](fact_energy_15m.md) rows where `time BETWEEN issue_start AND issue_end`.

fact_energy_15m.md

@@ -0,0 +1,30 @@
+---
+type: Parquet
+title: fact_energy_15m
+description: Unpivoted 15-min energy measurements (3 signals x 4 phases per asset window)
+resource: ./fact/fact_energy_15m.parquet
+tags: [fact, energy, timeseries, 15min]
+timestamp: 2024-12-31T07:30:00Z/2025-12-31T23:45:00Z
+---
+
+# fact_energy_15m
+
+Grain: one row per asset, signal, and 15-minute UTC window. The three source measures (`Energy_kWh_15m`, `Demand_kW`, `AvgPower_kW_15m`) x four phases (`L1`, `L2`, `L3`, `ALL`) are unpivoted so that each combination becomes a separate row with a single `value_float`.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `asset_id` | VARCHAR | FK -> [dim_asset](dim_asset.md). Anonymised asset identifier |
+| `signal_id` | INTEGER | FK -> [dim_signal_info](dim_signal_info.md). Identifies the signal (metric + phase) |
+| `time` | TIMESTAMP | Start of 15-min aggregation window (UTC) |
+| `value_float` | DOUBLE | Measured value. Unit and semantics determined by `signal_id` |
+| `observation_window_id` | INTEGER | FK -> [dim_observation_window](dim_observation_window.md). Coverage profile for this window |
+
+**Composite key:** (`asset_id`, `signal_id`, `time`)
+
+**Row count:** 12,076,111
+
+**Notes:**
+- Active power is magnitude-only (clipped to >= 0) for consumer assets; signed for supply meters (`mi_a`, `mi_b`).
+- Energy integration uses a forward-hold (piecewise-constant) model; no interpolation is applied.
+- The `ALL` phase is a synthetic aggregate summing L1 + L2 + L3.
+- To pivot back to wide format, join [dim_signal_info](dim_signal_info.md) and pivot on `signal_name` x `phase`.