---
id: "boomlet-1282"
slug: "boomlet-1282"
name: "Boomlet Smart Meter Building Energy"
domain: "Energy & Utilities"
taskType: "time_series_forecasting"
numRows: 16384
numColumns: 43
targetColumn: "target_0"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.3922"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/boomlet-1282/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1-8crpjGNI7o6tPQvOJyh-V-m0TA4k6H9WnLDEF3EOig/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/boomlet-1282"
---

# Dataset: Boomlet Smart Meter Building Energy (boomlet-1282)

> High-frequency 1-minute smart meter electrical load benchmark spanning 35 sub-meter channels across commercial facility infrastructure with a 60-minute forecast horizon to optimize peak shaving and demand response.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Energy & Utilities |
| **Target Column** | `target_0` |
| **Rows (Instances)** | 16,384 |
| **Features (Columns)** | 43 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.3922** |
| **Prediction Horizon** | 60 steps |
| **Seasonality** | 1440 |
| **Time Frequency** | 1-Minute |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Facility managers and commercial energy operators face steep demand charges and dynamic tariff spikes based on short-duration peak loads. Operating high-density compute, HVAC, and enterprise IT infrastructure requires continuous minute-by-minute visibility to orchestrate automated battery storage discharge, chiller sequencing, and dynamic load shed.
**Objective:** Forecast 35 synchronized sub-meter active power channels over a rolling 60-minute ahead horizon (H=60) at 1-minute resolution.
**Decision Impact:** Under-forecasting spikes incurs severe 15-minute peak ratchet tariffs and risks transformer tripping, while over-forecasting triggers unnecessary diesel generator spinning reserves or premature battery degradation costing tens of thousands in monthly OPEX.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 1 | 0 | Unique building and facility telemetry source identifier |
| `timestamp` | `datetime` | timestamp | 16,384 | 0 | ISO 8601 recording timestamp sampled at 1-minute cadence |
| `target_0` | `numeric` | target | 538 | 0 | Standardized electrical power telemetry for sub-meter channel 0 |
| `target_1` | `numeric` | target | 944 | 0 | Standardized electrical power telemetry for sub-meter channel 1 |
| `target_10` | `numeric` | target | 1,448 | 0 | Standardized electrical power telemetry for sub-meter channel 10 |
| `target_11` | `numeric` | target | 1,365 | 0 | Standardized electrical power telemetry for sub-meter channel 11 |
| `target_12` | `numeric` | target | 304 | 0 | Standardized electrical power telemetry for sub-meter channel 12 |
| `target_13` | `numeric` | target | 1,384 | 0 | Standardized electrical power telemetry for sub-meter channel 13 |
| `target_14` | `numeric` | target | 5,383 | 0 | Standardized electrical power telemetry for sub-meter channel 14 |
| `target_15` | `numeric` | target | 5,538 | 0 | Standardized electrical power telemetry for sub-meter channel 15 |
| `target_16` | `numeric` | target | 1,522 | 0 | Standardized electrical power telemetry for sub-meter channel 16 |
| `target_17` | `numeric` | target | 1,839 | 0 | Standardized electrical power telemetry for sub-meter channel 17 |
| `target_18` | `numeric` | target | 1,283 | 0 | Standardized electrical power telemetry for sub-meter channel 18 |
| `target_19` | `numeric` | target | 2,016 | 0 | Standardized electrical power telemetry for sub-meter channel 19 |
| `target_2` | `numeric` | target | 12,157 | 0 | Standardized electrical power telemetry for sub-meter channel 2 |
| `target_20` | `numeric` | target | 1,319 | 0 | Standardized electrical power telemetry for sub-meter channel 20 |
| `target_21` | `numeric` | target | 1,917 | 0 | Standardized electrical power telemetry for sub-meter channel 21 |
| `target_22` | `numeric` | target | 747 | 0 | Standardized electrical power telemetry for sub-meter channel 22 |
| `target_23` | `numeric` | target | 4,884 | 0 | Standardized electrical power telemetry for sub-meter channel 23 |
| `target_24` | `numeric` | target | 534 | 0 | Standardized electrical power telemetry for sub-meter channel 24 |
| `target_25` | `numeric` | target | 305 | 0 | Standardized electrical power telemetry for sub-meter channel 25 |
| `target_26` | `numeric` | target | 500 | 0 | Standardized electrical power telemetry for sub-meter channel 26 |
| `target_27` | `numeric` | target | 1,207 | 0 | Standardized electrical power telemetry for sub-meter channel 27 |
| `target_28` | `numeric` | target | 4,274 | 0 | Standardized electrical power telemetry for sub-meter channel 28 |
| `target_29` | `numeric` | target | 284 | 0 | Standardized electrical power telemetry for sub-meter channel 29 |
| `target_3` | `numeric` | target | 1,520 | 0 | Standardized electrical power telemetry for sub-meter channel 3 |
| `target_30` | `numeric` | target | 8,540 | 0 | Standardized electrical power telemetry for sub-meter channel 30 |
| `target_31` | `numeric` | target | 5,136 | 0 | Standardized electrical power telemetry for sub-meter channel 31 |
| `target_32` | `numeric` | target | 2,996 | 0 | Standardized electrical power telemetry for sub-meter channel 32 |
| `target_33` | `numeric` | target | 1,086 | 0 | Standardized electrical power telemetry for sub-meter channel 33 |
| `target_34` | `numeric` | target | 1,597 | 0 | Standardized electrical power telemetry for sub-meter channel 34 |
| `target_4` | `numeric` | target | 746 | 0 | Standardized electrical power telemetry for sub-meter channel 4 |
| `target_5` | `numeric` | target | 14,494 | 0 | Standardized electrical power telemetry for sub-meter channel 5 |
| `target_6` | `numeric` | target | 3,584 | 0 | Standardized electrical power telemetry for sub-meter channel 6 |
| `target_7` | `numeric` | target | 1,226 | 0 | Standardized electrical power telemetry for sub-meter channel 7 |
| `target_8` | `numeric` | target | 12,617 | 0 | Standardized electrical power telemetry for sub-meter channel 8 |
| `target_9` | `numeric` | target | 962 | 0 | Standardized electrical power telemetry for sub-meter channel 9 |
| `type` | `categorical` | static_covariate | 1 | 0 | Categorical facility primary usage and building classification code |
| `Application Usage` | `numeric` | static_covariate | 1 | 0 | Binary flag or proportion allocated to enterprise application servers |
| `Infrastructure` | `numeric` | static_covariate | 1 | 0 | Binary flag or proportion allocated to central facility infrastructure |
| `Database` | `numeric` | static_covariate | 1 | 0 | Binary flag or proportion allocated to dedicated database clusters |
| `Networking` | `numeric` | static_covariate | 1 | 0 | Binary flag or proportion allocated to network switches and routing gear |
| `Security` | `numeric` | static_covariate | 1 | 0 | Binary flag or proportion allocated to physical security and access control |

## 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-1282/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1-8crpjGNI7o6tPQvOJyh-V-m0TA4k6H9WnLDEF3EOig/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-1282](https://carlahq.eu/datasets/boomlet-1282)