---
id: "boomlet-619"
slug: "boomlet-619"
name: "Boomlet Smart Meter Building Energy"
domain: "Energy & Utilities"
taskType: "time_series_forecasting"
numRows: 16384
numColumns: 60
targetColumn: "target_0"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.2921"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/boomlet-619/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1_7iHke0dkjP0e7ow-638qzrxZJKyMxCkZ3f_ttxzf2E/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/boomlet-619"
---

# Dataset: Boomlet Smart Meter Building Energy (boomlet-619)

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

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Energy & Utilities |
| **Target Column** | `target_0` |
| **Rows (Instances)** | 16,384 |
| **Features (Columns)** | 60 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.2921** |
| **Prediction Horizon** | 60 steps |
| **Seasonality** | 1440 |
| **Time Frequency** | 1-Minute |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Operational environment requiring 60-step forward planning for Boomlet Smart Meter Building Energy.
**Objective:** Forecast target_0 over the next 60 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 | 16,384 | 0 | Time series variable 'timestamp' (timestamp). |
| `target_0` | `numeric` | target | 3,145 | 0 | Time series variable 'target_0' (target). |
| `target_1` | `numeric` | target | 143 | 0 | Time series variable 'target_1' (target). |
| `target_10` | `numeric` | target | 3,186 | 0 | Time series variable 'target_10' (target). |
| `target_11` | `numeric` | target | 199 | 0 | Time series variable 'target_11' (target). |
| `target_12` | `numeric` | target | 132 | 0 | Time series variable 'target_12' (target). |
| `target_13` | `numeric` | target | 3,449 | 0 | Time series variable 'target_13' (target). |
| `target_14` | `numeric` | target | 186 | 0 | Time series variable 'target_14' (target). |
| `target_15` | `numeric` | target | 195 | 0 | Time series variable 'target_15' (target). |
| `target_16` | `numeric` | target | 202 | 0 | Time series variable 'target_16' (target). |
| `target_17` | `numeric` | target | 2,927 | 0 | Time series variable 'target_17' (target). |
| `target_18` | `numeric` | target | 3,249 | 0 | Time series variable 'target_18' (target). |
| `target_19` | `numeric` | target | 3,078 | 0 | Time series variable 'target_19' (target). |
| `target_2` | `numeric` | target | 3,481 | 0 | Time series variable 'target_2' (target). |
| `target_20` | `numeric` | target | 3,056 | 0 | Time series variable 'target_20' (target). |
| `target_21` | `numeric` | target | 203 | 0 | Time series variable 'target_21' (target). |
| `target_22` | `numeric` | target | 195 | 0 | Time series variable 'target_22' (target). |
| `target_23` | `numeric` | target | 3,448 | 0 | Time series variable 'target_23' (target). |
| `target_24` | `numeric` | target | 186 | 0 | Time series variable 'target_24' (target). |
| `target_25` | `numeric` | target | 184 | 0 | Time series variable 'target_25' (target). |
| `target_26` | `numeric` | target | 3,241 | 0 | Time series variable 'target_26' (target). |
| `target_27` | `numeric` | target | 3,006 | 0 | Time series variable 'target_27' (target). |
| `target_28` | `numeric` | target | 3,105 | 0 | Time series variable 'target_28' (target). |
| `target_29` | `numeric` | target | 3,629 | 0 | Time series variable 'target_29' (target). |
| `target_3` | `numeric` | target | 3,513 | 0 | Time series variable 'target_3' (target). |
| `target_30` | `numeric` | target | 3,268 | 0 | Time series variable 'target_30' (target). |
| `target_31` | `numeric` | target | 3,223 | 0 | Time series variable 'target_31' (target). |
| `target_32` | `numeric` | target | 3,286 | 0 | Time series variable 'target_32' (target). |
| `target_33` | `numeric` | target | 202 | 0 | Time series variable 'target_33' (target). |
| `target_34` | `numeric` | target | 202 | 0 | Time series variable 'target_34' (target). |
| `target_35` | `numeric` | target | 206 | 0 | Time series variable 'target_35' (target). |
| `target_36` | `numeric` | target | 234 | 0 | Time series variable 'target_36' (target). |
| `target_37` | `numeric` | target | 190 | 0 | Time series variable 'target_37' (target). |
| `target_38` | `numeric` | target | 2,223 | 0 | Time series variable 'target_38' (target). |
| `target_39` | `numeric` | target | 183 | 0 | Time series variable 'target_39' (target). |
| `target_4` | `numeric` | target | 190 | 0 | Time series variable 'target_4' (target). |
| `target_40` | `numeric` | target | 207 | 0 | Time series variable 'target_40' (target). |
| `target_41` | `numeric` | target | 204 | 0 | Time series variable 'target_41' (target). |
| `target_42` | `numeric` | target | 141 | 0 | Time series variable 'target_42' (target). |
| `target_43` | `numeric` | target | 207 | 0 | Time series variable 'target_43' (target). |
| `target_44` | `numeric` | target | 185 | 0 | Time series variable 'target_44' (target). |
| `target_45` | `numeric` | target | 3,436 | 0 | Time series variable 'target_45' (target). |
| `target_46` | `numeric` | target | 194 | 0 | Time series variable 'target_46' (target). |
| `target_47` | `numeric` | target | 3,018 | 0 | Time series variable 'target_47' (target). |
| `target_48` | `numeric` | target | 3,396 | 0 | Time series variable 'target_48' (target). |
| `target_49` | `numeric` | target | 3,577 | 0 | Time series variable 'target_49' (target). |
| `target_5` | `numeric` | target | 3,520 | 0 | Time series variable 'target_5' (target). |
| `target_50` | `numeric` | target | 3,292 | 0 | Time series variable 'target_50' (target). |
| `target_51` | `numeric` | target | 3,322 | 0 | Time series variable 'target_51' (target). |
| `target_6` | `numeric` | target | 1,664 | 0 | Time series variable 'target_6' (target). |
| `target_7` | `numeric` | target | 3,226 | 0 | Time series variable 'target_7' (target). |
| `target_8` | `numeric` | target | 202 | 0 | Time series variable 'target_8' (target). |
| `target_9` | `numeric` | target | 3,513 | 0 | Time series variable 'target_9' (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-619/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1_7iHke0dkjP0e7ow-638qzrxZJKyMxCkZ3f_ttxzf2E/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-619](https://carlahq.eu/datasets/boomlet-619)