---
id: "boomlet-1676"
slug: "boomlet-1676"
name: "Boomlet Smart Meter Building Energy"
domain: "Energy & Utilities"
taskType: "time_series_forecasting"
numRows: 10463
numColumns: 108
targetColumn: "target_0"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.5453"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/boomlet-1676/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1LnWDsBH3KVPiNwx27lUoglAPz5M5Fe9DG6dhVtiiUU0/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/boomlet-1676"
---

# Dataset: Boomlet Smart Meter Building Energy (boomlet-1676)

> Boomlet Smart Meter Building Energy time series forecasting benchmark covering 1 series at Hourly cadence.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Energy & Utilities |
| **Target Column** | `target_0` |
| **Rows (Instances)** | 10,463 |
| **Features (Columns)** | 108 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.5453** |
| **Prediction Horizon** | 96 steps |
| **Seasonality** | 48 |
| **Time Frequency** | Hourly |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Operational environment requiring 96-step forward planning for Boomlet Smart Meter Building Energy.
**Objective:** Forecast target_0 over the next 96 time steps.
**Decision Impact:** High accuracy probabilistic forecasting minimizes under-forecasting penalties and over-forecasting holding costs.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 1 | 0 | Time series variable 'id' (identifier). |
| `timestamp` | `datetime` | timestamp | 10,463 | 0 | Time series variable 'timestamp' (timestamp). |
| `target_0` | `numeric` | target | 10,462 | 0 | Time series variable 'target_0' (target). |
| `target_1` | `numeric` | target | 10,461 | 0 | Time series variable 'target_1' (target). |
| `target_10` | `numeric` | target | 10,463 | 0 | Time series variable 'target_10' (target). |
| `target_11` | `numeric` | target | 10,462 | 0 | Time series variable 'target_11' (target). |
| `target_12` | `numeric` | target | 10,462 | 0 | Time series variable 'target_12' (target). |
| `target_13` | `numeric` | target | 10,462 | 0 | Time series variable 'target_13' (target). |
| `target_14` | `numeric` | target | 10,460 | 0 | Time series variable 'target_14' (target). |
| `target_15` | `numeric` | target | 10,459 | 0 | Time series variable 'target_15' (target). |
| `target_16` | `numeric` | target | 10,456 | 0 | Time series variable 'target_16' (target). |
| `target_17` | `numeric` | target | 10,249 | 0 | Time series variable 'target_17' (target). |
| `target_18` | `numeric` | target | 10,462 | 0 | Time series variable 'target_18' (target). |
| `target_19` | `numeric` | target | 10,459 | 0 | Time series variable 'target_19' (target). |
| `target_2` | `numeric` | target | 10,462 | 0 | Time series variable 'target_2' (target). |
| `target_20` | `numeric` | target | 10,460 | 0 | Time series variable 'target_20' (target). |
| `target_21` | `numeric` | target | 10,463 | 0 | Time series variable 'target_21' (target). |
| `target_22` | `numeric` | target | 10,461 | 0 | Time series variable 'target_22' (target). |
| `target_23` | `numeric` | target | 10,456 | 0 | Time series variable 'target_23' (target). |
| `target_24` | `numeric` | target | 10,455 | 0 | Time series variable 'target_24' (target). |
| `target_25` | `numeric` | target | 10,281 | 0 | Time series variable 'target_25' (target). |
| `target_26` | `numeric` | target | 10,463 | 0 | Time series variable 'target_26' (target). |
| `target_27` | `numeric` | target | 10,462 | 0 | Time series variable 'target_27' (target). |
| `target_28` | `numeric` | target | 10,326 | 0 | Time series variable 'target_28' (target). |
| `target_29` | `numeric` | target | 10,462 | 0 | Time series variable 'target_29' (target). |
| `target_3` | `numeric` | target | 6,557 | 0 | Time series variable 'target_3' (target). |
| `target_30` | `numeric` | target | 10,458 | 0 | Time series variable 'target_30' (target). |
| `target_31` | `numeric` | target | 10,449 | 0 | Time series variable 'target_31' (target). |
| `target_32` | `numeric` | target | 10,461 | 0 | Time series variable 'target_32' (target). |
| `target_33` | `numeric` | target | 10,460 | 0 | Time series variable 'target_33' (target). |
| `target_34` | `numeric` | target | 10,451 | 0 | Time series variable 'target_34' (target). |
| `target_35` | `numeric` | target | 10,449 | 0 | Time series variable 'target_35' (target). |
| `target_36` | `numeric` | target | 10,463 | 0 | Time series variable 'target_36' (target). |
| `target_37` | `numeric` | target | 10,425 | 0 | Time series variable 'target_37' (target). |
| `target_38` | `numeric` | target | 10,451 | 0 | Time series variable 'target_38' (target). |
| `target_39` | `numeric` | target | 10,255 | 0 | Time series variable 'target_39' (target). |
| `target_4` | `numeric` | target | 10,462 | 0 | Time series variable 'target_4' (target). |
| `target_40` | `numeric` | target | 10,348 | 0 | Time series variable 'target_40' (target). |
| `target_41` | `numeric` | target | 10,453 | 0 | Time series variable 'target_41' (target). |
| `target_42` | `numeric` | target | 10,463 | 0 | Time series variable 'target_42' (target). |
| `target_43` | `numeric` | target | 10,450 | 0 | Time series variable 'target_43' (target). |
| `target_44` | `numeric` | target | 10,388 | 0 | Time series variable 'target_44' (target). |
| `target_45` | `numeric` | target | 10,462 | 0 | Time series variable 'target_45' (target). |
| `target_46` | `numeric` | target | 10,463 | 0 | Time series variable 'target_46' (target). |
| `target_47` | `numeric` | target | 10,461 | 0 | Time series variable 'target_47' (target). |
| `target_48` | `numeric` | target | 10,451 | 0 | Time series variable 'target_48' (target). |
| `target_49` | `numeric` | target | 10,459 | 0 | Time series variable 'target_49' (target). |
| `target_5` | `numeric` | target | 10,428 | 0 | Time series variable 'target_5' (target). |
| `target_50` | `numeric` | target | 10,461 | 0 | Time series variable 'target_50' (target). |
| `target_51` | `numeric` | target | 10,428 | 0 | Time series variable 'target_51' (target). |
| `target_52` | `numeric` | target | 10,461 | 0 | Time series variable 'target_52' (target). |
| `target_53` | `numeric` | target | 10,455 | 0 | Time series variable 'target_53' (target). |
| `target_54` | `numeric` | target | 10,463 | 0 | Time series variable 'target_54' (target). |
| `target_55` | `numeric` | target | 10,461 | 0 | Time series variable 'target_55' (target). |
| `target_56` | `numeric` | target | 10,351 | 0 | Time series variable 'target_56' (target). |
| `target_57` | `numeric` | target | 10,351 | 0 | Time series variable 'target_57' (target). |
| `target_58` | `numeric` | target | 10,459 | 0 | Time series variable 'target_58' (target). |
| `target_59` | `numeric` | target | 10,442 | 0 | Time series variable 'target_59' (target). |
| `target_6` | `numeric` | target | 10,462 | 0 | Time series variable 'target_6' (target). |
| `target_60` | `numeric` | target | 10,404 | 0 | Time series variable 'target_60' (target). |
| `target_61` | `numeric` | target | 10,458 | 0 | Time series variable 'target_61' (target). |
| `target_62` | `numeric` | target | 10,455 | 0 | Time series variable 'target_62' (target). |
| `target_63` | `numeric` | target | 10,462 | 0 | Time series variable 'target_63' (target). |
| `target_64` | `numeric` | target | 10,325 | 0 | Time series variable 'target_64' (target). |
| `target_65` | `numeric` | target | 10,462 | 0 | Time series variable 'target_65' (target). |
| `target_66` | `numeric` | target | 10,461 | 0 | Time series variable 'target_66' (target). |
| `target_67` | `numeric` | target | 10,349 | 0 | Time series variable 'target_67' (target). |
| `target_68` | `numeric` | target | 10,460 | 0 | Time series variable 'target_68' (target). |
| `target_69` | `numeric` | target | 10,459 | 0 | Time series variable 'target_69' (target). |
| `target_7` | `numeric` | target | 10,455 | 0 | Time series variable 'target_7' (target). |
| `target_70` | `numeric` | target | 10,451 | 0 | Time series variable 'target_70' (target). |
| `target_71` | `numeric` | target | 10,241 | 0 | Time series variable 'target_71' (target). |
| `target_72` | `numeric` | target | 10,459 | 0 | Time series variable 'target_72' (target). |
| `target_73` | `numeric` | target | 10,462 | 0 | Time series variable 'target_73' (target). |
| `target_74` | `numeric` | target | 10,459 | 0 | Time series variable 'target_74' (target). |
| `target_75` | `numeric` | target | 10,458 | 0 | Time series variable 'target_75' (target). |
| `target_76` | `numeric` | target | 10,460 | 0 | Time series variable 'target_76' (target). |
| `target_77` | `numeric` | target | 10,210 | 0 | Time series variable 'target_77' (target). |
| `target_78` | `numeric` | target | 10,460 | 0 | Time series variable 'target_78' (target). |
| `target_79` | `numeric` | target | 10,461 | 0 | Time series variable 'target_79' (target). |
| `target_8` | `numeric` | target | 10,460 | 0 | Time series variable 'target_8' (target). |
| `target_80` | `numeric` | target | 10,452 | 0 | Time series variable 'target_80' (target). |
| `target_81` | `numeric` | target | 10,458 | 0 | Time series variable 'target_81' (target). |
| `target_82` | `numeric` | target | 10,306 | 0 | Time series variable 'target_82' (target). |
| `target_83` | `numeric` | target | 10,335 | 0 | Time series variable 'target_83' (target). |
| `target_84` | `numeric` | target | 10,463 | 0 | Time series variable 'target_84' (target). |
| `target_85` | `numeric` | target | 10,448 | 0 | Time series variable 'target_85' (target). |
| `target_86` | `numeric` | target | 10,460 | 0 | Time series variable 'target_86' (target). |
| `target_87` | `numeric` | target | 10,462 | 0 | Time series variable 'target_87' (target). |
| `target_88` | `numeric` | target | 9,391 | 0 | Time series variable 'target_88' (target). |
| `target_89` | `numeric` | target | 10,226 | 0 | Time series variable 'target_89' (target). |
| `target_9` | `numeric` | target | 10,463 | 0 | Time series variable 'target_9' (target). |
| `target_90` | `numeric` | target | 10,453 | 0 | Time series variable 'target_90' (target). |
| `target_91` | `numeric` | target | 10,396 | 0 | Time series variable 'target_91' (target). |
| `target_92` | `numeric` | target | 10,461 | 0 | Time series variable 'target_92' (target). |
| `target_93` | `numeric` | target | 10,453 | 0 | Time series variable 'target_93' (target). |
| `target_94` | `numeric` | target | 10,462 | 0 | Time series variable 'target_94' (target). |
| `target_95` | `numeric` | target | 10,457 | 0 | Time series variable 'target_95' (target). |
| `target_96` | `numeric` | target | 10,360 | 0 | Time series variable 'target_96' (target). |
| `target_97` | `numeric` | target | 10,448 | 0 | Time series variable 'target_97' (target). |
| `target_98` | `numeric` | target | 10,462 | 0 | Time series variable 'target_98' (target). |
| `target_99` | `numeric` | target | 10,332 | 0 | Time series variable 'target_99' (target). |
| `type` | `categorical` | static_covariate | 1 | 0 | Time series variable 'type' (static_covariate). |
| `Application Usage` | `numeric` | static_covariate | 1 | 0 | Time series variable 'Application Usage' (static_covariate). |
| `Infrastructure` | `numeric` | static_covariate | 1 | 0 | Time series variable 'Infrastructure' (static_covariate). |
| `Database` | `numeric` | static_covariate | 1 | 0 | Time series variable 'Database' (static_covariate). |
| `Networking` | `numeric` | static_covariate | 1 | 0 | Time series variable 'Networking' (static_covariate). |
| `Security` | `numeric` | static_covariate | 1 | 0 | Time series variable 'Security' (static_covariate). |

## FEV Benchmark Leaderboard (AutoGluon Time-Series Suite)
Empirical probabilistic evaluation across standard foundation models and baselines:

| Rank | Method / Model | Family | Organization | SQL Win Rate (%) | SQL Skill Score | MASE Win Rate (%) | Median Latency | Execution |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **Timestral-209M** | Foundation Model | — | 87.7% | 50.39 | 86.0% | 0.51s | Server (CUDA / PyTorch) |
| 2 | **Timestral-123M (Carla Engine)** | Foundation Model | — | 86.8% | 50.18 | 85.0% | 0.32s | WebGPU (Browser / Sheets) |
| 3 | **TimeRSI** | Closed-Source API | — | 84.8% | 51.24 | 83.8% | 2512s | Server (CUDA / PyTorch) |
| 4 | **t0-gamma** | Foundation Model | — | 84.4% | 49.42 | 81.8% | 4.38s | Server (CUDA / PyTorch) |
| 5 | **Timestral-28M** | Foundation Model | — | 79.2% | 48.42 | 77.0% | 0.11s | Server (CUDA / PyTorch) |
| 6 | **timesfm-3** | Foundation Model | — | 78.5% | 48.99 | 77.0% | 3.41s | Server (CUDA / PyTorch) |
| 7 | **ChakraTS** | Baseline | — | 78.2% | 48.14 | 79.5% | 14.22s | Server (CUDA / PyTorch) |
| 8 | **Lingjiang2.0** | Baseline | — | 73.9% | 47.02 | 71.3% | 8.89s | Server (CUDA / PyTorch) |
| 9 | **Chronos-2** | Foundation Model | — | 70.4% | 47.28 | 68.4% | 0.84s | Server (CUDA / PyTorch) |
| 10 | **timeh2o-v1-tiny** | Foundation Model | — | 69.9% | 45.49 | 67.0% | 0.19s | Server (CUDA / PyTorch) |
| 11 | **Timestral-5M** | Foundation Model | — | 69.0% | 46.19 | 67.7% | 0.04s | Server (CUDA / PyTorch) |
| 12 | **Toto-2.0-2.5B** | Baseline | — | 67.5% | 44.42 | 65.7% | 5.58s | Server (CUDA / PyTorch) |
| 13 | **t0-beta** | Foundation Model | — | 67.4% | 46.65 | 64.5% | 2.29s | Server (CUDA / PyTorch) |
| 14 | **Toto-2.0-1B** | Baseline | — | 66.4% | 44.43 | 65.5% | 2.48s | Server (CUDA / PyTorch) |
| 15 | **TiRex-2** | Foundation Model | — | 65.8% | 45.50 | 63.4% | 0.27s | Server (CUDA / PyTorch) |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/boomlet-1676/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1LnWDsBH3KVPiNwx27lUoglAPz5M5Fe9DG6dhVtiiUU0/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://doi.org/10.1038/s41597-020-00717-4)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/boomlet-1676](https://carlahq.eu/datasets/boomlet-1676)