Energy & UtilitiesNACE D35Time Series Forecasting1-Minute Cadence60 Steps HorizonTarget:
target_0fev / AutoGluon ↗Boomlet Smart Meter Building Energy
Boomlet Smart Meter Building Energy time series forecasting benchmark covering 1 series at 1-Minute cadence.
16,384Records (Rows)
1Time Series
1-MinuteSampling Cadence
60 StepsForecast Horizon
Period 1440Dominant Seasonality
41 Methodsfev Leaderboard (SQL)
BUSINESS CONTEXT & OBJECTIVESmart Buildings & Energy Management (D35)
Business Objective: Boomlet Smart Meter Building Energy
01Business Context
Operational environment requiring 60-step forward planning for Boomlet Smart Meter Building Energy.
02Analytical Objective
Forecast target_0 over the next 60 time steps.
03Economic & Decision Impact
High accuracy probabilistic forecasting minimizes under-forecasting penalties and over-forecasting holding costs.
ML Benchmark Narrative
Evaluated under rolling window regime with 20 windows using SQL loss.
Source Origin:Building Data Genome 2 / Smart Meter Benchmark
Creator:AutoGluon / Academic Benchmarking Community (2025)
License:Open Data / Apache-2.0
Reference:Paper / Repo ↗
FEATURE SPECIFICATION & DATA DICTIONARY
Target & Feature Column Definitions
Exhaustive business definitions, measurement units, roles, and target variables across all 60 columns.
| Col | Variable / Feature Name | Role | Type | Unit / Scale | Description & Business Meaning |
|---|---|---|---|---|---|
| C | target_0Target 0 | TARGET | NUM | — | Time series variable 'target_0' (target). |
| D | target_1Target 1 | TARGET | NUM | — | Time series variable 'target_1' (target). |
| E | target_10Target 10 | TARGET | NUM | — | Time series variable 'target_10' (target). |
| F | target_11Target 11 | TARGET | NUM | — | Time series variable 'target_11' (target). |
| G | target_12Target 12 | TARGET | NUM | — | Time series variable 'target_12' (target). |
| H | target_13Target 13 | TARGET | NUM | — | Time series variable 'target_13' (target). |
| I | target_14Target 14 | TARGET | NUM | — | Time series variable 'target_14' (target). |
| J | target_15Target 15 | TARGET | NUM | — | Time series variable 'target_15' (target). |
| K | target_16Target 16 | TARGET | NUM | — | Time series variable 'target_16' (target). |
| L | target_17Target 17 | TARGET | NUM | — | Time series variable 'target_17' (target). |
| M | target_18Target 18 | TARGET | NUM | — | Time series variable 'target_18' (target). |
| N | target_19Target 19 | TARGET | NUM | — | Time series variable 'target_19' (target). |
| O | target_2Target 2 | TARGET | NUM | — | Time series variable 'target_2' (target). |
| P | target_20Target 20 | TARGET | NUM | — | Time series variable 'target_20' (target). |
| Q | target_21Target 21 | TARGET | NUM | — | Time series variable 'target_21' (target). |
| R | target_22Target 22 | TARGET | NUM | — | Time series variable 'target_22' (target). |
| S | target_23Target 23 | TARGET | NUM | — | Time series variable 'target_23' (target). |
| T | target_24Target 24 | TARGET | NUM | — | Time series variable 'target_24' (target). |
| U | target_25Target 25 | TARGET | NUM | — | Time series variable 'target_25' (target). |
| V | target_26Target 26 | TARGET | NUM | — | Time series variable 'target_26' (target). |
| W | target_27Target 27 | TARGET | NUM | — | Time series variable 'target_27' (target). |
| X | target_28Target 28 | TARGET | NUM | — | Time series variable 'target_28' (target). |
| Y | target_29Target 29 | TARGET | NUM | — | Time series variable 'target_29' (target). |
| Z | target_3Target 3 | TARGET | NUM | — | Time series variable 'target_3' (target). |
| AA | target_30Target 30 | TARGET | NUM | — | Time series variable 'target_30' (target). |
| AB | target_31Target 31 | TARGET | NUM | — | Time series variable 'target_31' (target). |
| AC | target_32Target 32 | TARGET | NUM | — | Time series variable 'target_32' (target). |
| AD | target_33Target 33 | TARGET | NUM | — | Time series variable 'target_33' (target). |
| AE | target_34Target 34 | TARGET | NUM | — | Time series variable 'target_34' (target). |
| AF | target_35Target 35 | TARGET | NUM | — | Time series variable 'target_35' (target). |
| AG | target_36Target 36 | TARGET | NUM | — | Time series variable 'target_36' (target). |
| AH | target_37Target 37 | TARGET | NUM | — | Time series variable 'target_37' (target). |
| AI | target_38Target 38 | TARGET | NUM | — | Time series variable 'target_38' (target). |
| AJ | target_39Target 39 | TARGET | NUM | — | Time series variable 'target_39' (target). |
| AK | target_4Target 4 | TARGET | NUM | — | Time series variable 'target_4' (target). |
| AL | target_40Target 40 | TARGET | NUM | — | Time series variable 'target_40' (target). |
| AM | target_41Target 41 | TARGET | NUM | — | Time series variable 'target_41' (target). |
| AN | target_42Target 42 | TARGET | NUM | — | Time series variable 'target_42' (target). |
| AO | target_43Target 43 | TARGET | NUM | — | Time series variable 'target_43' (target). |
| AP | target_44Target 44 | TARGET | NUM | — | Time series variable 'target_44' (target). |
| AQ | target_45Target 45 | TARGET | NUM | — | Time series variable 'target_45' (target). |
| AR | target_46Target 46 | TARGET | NUM | — | Time series variable 'target_46' (target). |
| AS | target_47Target 47 | TARGET | NUM | — | Time series variable 'target_47' (target). |
| AT | target_48Target 48 | TARGET | NUM | — | Time series variable 'target_48' (target). |
| AU | target_49Target 49 | TARGET | NUM | — | Time series variable 'target_49' (target). |
| AV | target_5Target 5 | TARGET | NUM | — | Time series variable 'target_5' (target). |
| AW | target_50Target 50 | TARGET | NUM | — | Time series variable 'target_50' (target). |
| AX | target_51Target 51 | TARGET | NUM | — | Time series variable 'target_51' (target). |
| AY | target_6Target 6 | TARGET | NUM | — | Time series variable 'target_6' (target). |
| AZ | target_7Target 7 | TARGET | NUM | — | Time series variable 'target_7' (target). |
| BA | target_8Target 8 | TARGET | NUM | — | Time series variable 'target_8' (target). |
| BB | target_9Target 9 | TARGET | NUM | — | Time series variable 'target_9' (target). |
| A | idId | SERIES ID | CAT | — | Time series variable 'id' (identifier). |
| B | timestampTimestamp | TIME | TIME | — | Time series variable 'timestamp' (timestamp). |
| BC | typeType | Feature | CAT | — | Time series variable 'type' (static_covariate). |
| BD | Application Usage | Feature | NUM | — | Time series variable 'Application Usage' (static_covariate). |
| BE | Infrastructure | Feature | NUM | — | Time series variable 'Infrastructure' (static_covariate). |
| BF | Database | Feature | NUM | — | Time series variable 'Database' (static_covariate). |
| BG | Networking | Feature | NUM | — | Time series variable 'Networking' (static_covariate). |
| BH | Security | Feature | NUM | — | Time series variable 'Security' (static_covariate). |
DATASET PREVIEW
Interactive Data Table
Explore rows, feature values, and target labels for Boomlet Smart Meter Building Energy.
TIME SERIES FORECASTING LEADERBOARD (FEV-BENCH)
Foundation Models vs Traditional ML & Baselines (41 Methods)
Empirical evaluation directly from the AutoGluon fev benchmark suite evaluating zero-shot foundation models against deep learning, gradient-boosted trees, and statistical baselines across 100 evaluation tasks.
#1 Global Ranked Model?Timestral-209M achieves the highest aggregate win rate (87.7%) across all 100 tasks in fev-bench.
Timestral-209M87.7% Win Rate • 0.51s latencyEvaluated Methods?41 models spanning open-weight foundation models, closed APIs, deep neural architectures, and statistical baselines.
41 MethodsMistral AI, Google, AWS, Salesforce, Prior LabsPrimary Evaluation Metric?Scaled Quantile Loss (SQL) penalizes both point prediction errors and quantile miscalibration across 9 probabilistic levels (p10 to p90).
SQL & MASEProbabilistic calibrationBrowser Execution?Carla Engine executes Timestral-123M 100% locally in-browser via WebGPU tensor kernels with zero server uploads.
100% WebGPUTimestral-123M • Zero server upload| Rank | Method / Model | Family | Organization | SQL Win Rate?Average win rate (%) against all competing models across the benchmark tasks under Scaled Quantile Loss. | SQL Skill?Normalized skill score relative to the Seasonal Naive baseline (0% = baseline, higher is better). | MASE Win Rate?Average win rate (%) under Mean Absolute Scaled Error. | Latency?Median end-to-end execution time per 100 time series in seconds. | Link |
|---|---|---|---|---|---|---|---|---|
| #1 | Timestral-209M | Foundation Model | Mistral AI | 87.7% | +50.4% | 86.0% | 0.51s | Repo ↗ |
| #2 | Timestral-123M (Carla Engine) | Foundation Model | Mistral AI | 86.8% | +50.2% | 85.0% | 0.32s | Repo ↗ |
| #3 | TimeRSI | Closed-Source API | GaTech & SamayLabs | 84.8% | +51.2% | 83.8% | 2512s | Repo ↗ |
| #4 | t0-gamma | Foundation Model | The Forecasting Company | 84.4% | +49.4% | 81.8% | 4.38s | Repo ↗ |
| #5 | Timestral-28M | Foundation Model | Mistral AI | 79.2% | +48.4% | 77.0% | 0.11s | Repo ↗ |
| #6 | timesfm-3 | Foundation Model | 78.5% | +49.0% | 77.0% | 3.41s | Repo ↗ | |
| #7 | ChakraTS | Baseline | Community | 78.2% | +48.1% | 79.5% | 14.22s | Repo ↗ |
| #8 | Lingjiang2.0 | Baseline | Community | 73.9% | +47.0% | 71.3% | 8.89s | Repo ↗ |
| #9 | Chronos-2 | Foundation Model | AWS | 70.4% | +47.3% | 68.4% | 0.84s | Repo ↗ |
| #10 | timeh2o-v1-tiny | Foundation Model | H2O.ai | 69.9% | +45.5% | 67.0% | 0.19s | Repo ↗ |
| #11 | Timestral-5M | Foundation Model | Mistral AI | 69.0% | +46.2% | 67.7% | 40ms | Repo ↗ |
| #12 | Toto-2.0-2.5B | Baseline | Community | 67.5% | +44.4% | 65.7% | 5.58s | Repo ↗ |
| #13 | t0-beta | Foundation Model | The Forecasting Company | 67.4% | +46.6% | 64.5% | 2.29s | Repo ↗ |
| #14 | Toto-2.0-1B | Baseline | Community | 66.4% | +44.4% | 65.5% | 2.48s | Repo ↗ |
| #15 | TiRex-2 | Foundation Model | NX-AI | 65.8% | +45.5% | 63.4% | 0.27s | Repo ↗ |
| #16 | Toto-2.0-313m | Baseline | Community | 65.1% | +44.2% | 63.5% | 1.06s | Repo ↗ |
| #17 | TiRex | Foundation Model | NX-AI | 58.0% | +42.6% | 54.1% | 0.24s | Repo ↗ |
| #18 | Toto-2.0-22m | Baseline | Community | 57.6% | +42.9% | 56.0% | 0.25s | Repo ↗ |
| #19 | TimesFM-2.5 | Baseline | Community | 54.6% | +42.2% | 53.5% | 1.87s | Repo ↗ |
| #20 | TabPFN-TS-3 | Foundation Model | Prior Labs | 52.4% | +43.1% | 51.3% | 235s | Repo ↗ |
| #21 | TS-ICL | Foundation Model | EDF-Lab | 51.2% | +43.1% | 49.8% | 2.49s | Repo ↗ |
| #22 | FlowState | Foundation Model | IBM | 49.3% | +39.9% | 49.9% | 2.29s | Repo ↗ |
| #23 | citras-fm | Foundation Model | Hitachi | 48.8% | +41.2% | 46.9% | 0.60s | Repo ↗ |
| #24 | Toto-1.0 | Baseline | Community | 47.5% | +40.8% | 45.9% | 22.35s | Repo ↗ |
| #25 | TabPFN-TS | Foundation Model | Prior Labs | 47.3% | +41.5% | 45.4% | 109s | Repo ↗ |
| #26 | Toto-2.0-4m | Baseline | Community | 46.2% | +40.7% | 43.6% | 0.20s | Repo ↗ |
| #27 | Moirai-2.0 | Baseline | Community | 41.9% | +39.3% | 40.5% | 0.35s | Repo ↗ |
| #28 | Chronos-Bolt | Foundation Model | AWS | 41.4% | +38.9% | 39.3% | 0.26s | Repo ↗ |
| #29 | TFT | Deep Learning | — | 31.7% | +32.2% | 29.2% | 829s | Repo ↗ |
| #30 | Stat. Ensemble | Baseline | Community | 27.5% | +20.7% | 30.6% | 147s | Repo ↗ |
| #31 | Sundial-Base | Foundation Model | Tsinghua University | 27.4% | +33.4% | 33.6% | 8.01s | Repo ↗ |
| #32 | PatchTST | Deep Learning | — | 26.8% | +30.1% | 25.1% | 664s | Repo ↗ |
| #33 | DeepAR | Deep Learning | — | 26.1% | +29.1% | 23.6% | 1113s | Repo ↗ |
| #34 | AutoARIMA | Statistical Baseline | — | 23.3% | +21.3% | 21.5% | 20.14s | Repo ↗ |
| #35 | AutoETS | Statistical Baseline | — | 20.3% | -26.8% | 22.5% | 3.48s | Repo ↗ |
| #36 | CatBoost | Tree Ensemble | — | 19.1% | +23.0% | 35.1% | 29.48s | Repo ↗ |
| #37 | LightGBM | Tree Ensemble | — | 17.8% | +21.0% | 33.1% | 3.03s | Repo ↗ |
| #38 | AutoTheta | Statistical Baseline | — | 15.6% | +5.5% | 19.8% | 3.28s | Repo ↗ |
| #39 | Seasonal Naive | Baseline | Community | 10.3% | 0.0% | 11.8% | 0.48s | Repo ↗ |
| #40 | Naive | Statistical Baseline | — | 6.7% | -45.4% | 10.9% | 0.47s | Repo ↗ |
| #41 | Drift | Statistical Baseline | — | 6.1% | -45.8% | 10.0% | 0.45s | Repo ↗ |
STATISTICAL DISTRIBUTIONS
Variable Schema & Summary Distributions
Observed numerical ranges, category cardinalities, missing rates, and sample values across 16,384 rows.
| Col | Variable Name | Type | Missing | Distinct | Summary Stats / Distribution | Sample Values |
|---|---|---|---|---|---|---|
| C | target_0TARGET | NUM | 0 | 3,145 | [-1.8833141326904297, 2.3024609088897705] μ=0.0 σ=1.0 | 1.12809514999389651.17842507362365721.228754997253418 |
| D | target_1TARGET | NUM | 0 | 143 | [-2.3401710987091064, 46.37258529663086] μ=-0.0 σ=1.0 | -0.75374031066894530.45941257476806640.4594125747680664 |
| E | target_10TARGET | NUM | 0 | 3,186 | [-1.868138313293457, 2.44521427154541] μ=0.0 σ=1.0 | 0.84698307514190670.90046542882919310.9539477825164795 |
| F | target_11TARGET | NUM | 0 | 199 | [-1.1590602397918701, 43.196319580078125] μ=0.0 σ=1.0 | -0.84326142072677610.075426109135150910.04671712592244148 |
| G | target_12TARGET | NUM | 0 | 132 | [-1.6180280447006226, 49.059043884277344] μ=0.0 σ=1.0 | 0.68697208166122441.14797210693359380.32475775480270386 |
| H | target_13TARGET | NUM | 0 | 3,449 | [-1.8929290771484375, 2.5460054874420166] μ=-0.0 σ=1.0 | 0.68143892288208010.73494154214859010.7884441614151001 |
| I | target_14TARGET | NUM | 0 | 186 | [-1.8769103288650513, 49.92314147949219] μ=0.1 σ=1.0 | 0.242182746529579160.78036510944366460.006727964151650667 |
| J | target_15TARGET | NUM | 0 | 195 | [-1.3127145767211914, 48.03789138793945] μ=0.0 σ=1.0 | 0.404673576354980470.25885757803916930.1292433887720108 |
| K | target_16TARGET | NUM | 0 | 202 | [-1.5062695741653442, 53.66469955444336] μ=-0.0 σ=1.0 | -0.4428631067276001-0.100410170853137970.47635263204574585 |
| L | target_17TARGET | NUM | 0 | 2,927 | [-1.875279188156128, 2.2515692710876465] μ=-0.0 σ=1.0 | 1.7365534305572511.7869628667831421.8373721837997437 |
| M | target_18TARGET | NUM | 0 | 3,249 | [-1.867404818534851, 2.520840644836426] μ=0.0 σ=1.0 | 0.80055552721023560.85414701700210570.907738447189331 |
| N | target_19TARGET | NUM | 0 | 3,078 | [-1.8682246208190918, 2.2488138675689697] μ=-0.0 σ=1.0 | 1.091605782508851.142028808593751.19245183467865 |
| O | target_2TARGET | NUM | 0 | 3,481 | [-1.8890480995178223, 2.365147590637207] μ=-0.0 σ=1.0 | 0.72295868396759030.77639216184616090.8298256397247314 |
| P | target_20TARGET | NUM | 0 | 3,056 | [-1.8724390268325806, 2.3270347118377686] μ=0.0 σ=1.0 | 1.07906603813171391.12959110736846921.180116057395935 |
| Q | target_21TARGET | NUM | 0 | 203 | [-1.519518256187439, 46.39919662475586] μ=0.0 σ=1.0 | -1.17845976352691650.61209714412689210.28809159994125366 |
| R | target_22TARGET | NUM | 0 | 195 | [-1.4970980882644653, 52.142547607421875] μ=-0.0 σ=1.0 | 0.0457144938409328460.97857791185379030.8350604772567749 |
| S | target_23TARGET | NUM | 0 | 3,448 | [-1.8875648975372314, 2.500114917755127] μ=-0.0 σ=1.0 | 0.83628255128860470.88963901996612550.9429954886436462 |
| T | target_24TARGET | NUM | 0 | 186 | [-1.8683239221572876, 54.07149887084961] μ=-0.0 σ=1.0 | -0.1963084340095520.421610355377197270.7487438321113586 |
| U | target_25TARGET | NUM | 0 | 184 | [-1.5028076171875, 43.83976745605469] μ=0.0 σ=1.0 | -0.2649058401584625-0.51248621940612790.01804313063621521 |
| V | target_26TARGET | NUM | 0 | 3,241 | [-1.8727586269378662, 2.349658489227295] μ=0 σ=1.0 | 1.08410441875457761.1342916488647461.1844788789749146 |
| W | target_27TARGET | NUM | 0 | 3,006 | [-1.8697007894515991, 2.3014440536499023] μ=-0.0 σ=1.0 | 1.75446367263793951.80479931831359861.8551349639892578 |
| X | target_28TARGET | NUM | 0 | 3,105 | [-1.8855963945388794, 2.4781761169433594] μ=0.0 σ=1.0 | 0.76643961668014530.81976473331451420.8730898499488831 |
| Y | target_29TARGET | NUM | 0 | 3,629 | [-1.827337384223938, 2.0296216011047363] μ=0.0 σ=1.0 | 1.14150595664978031.19225537776947021.2430049180984497 |
| Z | target_3TARGET | NUM | 0 | 3,513 | [-1.883399486541748, 2.2492403984069824] μ=-0.0 σ=1.0 | 0.69871890544891360.75230830907821660.8058977723121643 |
| AA | target_30TARGET | NUM | 0 | 3,268 | [-1.861322283744812, 2.3991646766662598] μ=0.0 σ=1.0 | 1.07306396961212161.12345409393310551.1738442182540894 |
| AB | target_31TARGET | NUM | 0 | 3,223 | [-1.8655445575714111, 2.369386911392212] μ=-0.0 σ=1 | 1.08714377880096441.13755953311920171.1879754066467285 |
| AC | target_32TARGET | NUM | 0 | 3,286 | [-1.8817945718765259, 2.358286142349243] μ=-0.0 σ=1.0 | 0.70180886983871460.75550341606140140.8091980218887329 |
| AD | target_33TARGET | NUM | 0 | 202 | [-1.853738784790039, 51.9370231628418] μ=-0.0 σ=1.0 | -0.25198513269424440.08244694769382477-0.5512138605117798 |
| AE | target_34TARGET | NUM | 0 | 202 | [-1.1618269681930542, 43.21357727050781] μ=0.0 σ=1.0 | -0.84588557481765750.073216661810874940.04449471831321716 |
| AF | target_35TARGET | NUM | 0 | 206 | [-2.2272582054138184, 48.34104919433594] μ=0.1 σ=1.0 | -0.5448834300041199-0.237526476383209230.24777397513389587 |
| AG | target_36TARGET | NUM | 0 | 234 | [-2.373337507247925, 48.771846771240234] μ=0.1 σ=1.0 | 1.09759318828582761.02953577041625981.0975931882858276 |
| AH | target_37TARGET | NUM | 0 | 190 | [-1.3151854276657104, 45.43000411987305] μ=0.0 σ=1.0 | 0.92038106918334960.75229334831237790.8195284605026245 |
| AI | target_38TARGET | NUM | 0 | 2,223 | [-1.7899861335754395, 2.171778440475464] μ=-0.0 σ=1.0 | 0.81964039802551270.87372589111328120.9278114438056946 |
| AJ | target_39TARGET | NUM | 0 | 183 | [-1.8917714357376099, 43.886085510253906] μ=0.0 σ=1.0 | -0.26317015290260315-0.493298590183258060.07317140698432922 |
| AK | target_4TARGET | NUM | 0 | 190 | [-1.3887423276901245, 43.83798599243164] μ=-0.0 σ=1.0 | -0.4762883484363556-0.0286693945527076720.07462728768587112 |
| AL | target_40TARGET | NUM | 0 | 207 | [-1.559283971786499, 43.30224609375] μ=-0.0 σ=1.0 | 0.36894834041595460.007404765579849482-0.11310975253582001 |
| AM | target_41TARGET | NUM | 0 | 204 | [-1.9204832315444946, 50.24440383911133] μ=0.0 σ=1.0 | 0.60937124490737921.3220063447952270.46684423089027405 |
| AN | target_42TARGET | NUM | 0 | 141 | [-1.3352755308151245, 51.8422966003418] μ=0.0 σ=1.0 | 0.091352805495262150.5193413496017456-0.4436328411102295 |
| AO | target_43TARGET | NUM | 0 | 207 | [-1.2236402034759521, 41.716819763183594] μ=0.1 σ=1.0 | -0.009260611608624458-0.07402752339839935-0.2521365284919739 |
| AP | target_44TARGET | NUM | 0 | 185 | [-1.932262659072876, 50.6822395324707] μ=0.0 σ=1.0 | 0.34912499785423280.0639515370130539-0.042988505214452744 |
| AQ | target_45TARGET | NUM | 0 | 3,436 | [-1.889270544052124, 2.6734793186187744] μ=-0.0 σ=1.0 | 0.6873516440391540.7407900691032410.7942284941673279 |
| AR | target_46TARGET | NUM | 0 | 194 | [-1.7895991802215576, 52.13898849487305] μ=-0.0 σ=1.0 | 0.0403130315244197850.97320944070816040.8296869397163391 |
| AS | target_47TARGET | NUM | 0 | 3,018 | [-1.8601871728897095, 2.755176305770874] μ=-0.0 σ=1.0 | 1.12839484214782711.17870759963989261.2290204763412476 |
| AT | target_48TARGET | NUM | 0 | 3,396 | [-1.8778685331344604, 2.3996706008911133] μ=-0.0 σ=1.0 | 1.01067388057708741.06416535377502441.117656946182251 |
| AU | target_49TARGET | NUM | 0 | 3,577 | [-1.8747949600219727, 2.6669576168060303] μ=0.0 σ=1.0 | 1.01087844371795651.0643002986907961.1177222728729248 |
| AV | target_5TARGET | NUM | 0 | 3,520 | [-1.9009629487991333, 2.1824381351470947] μ=-0.0 σ=1.0 | 0.72911500930786130.78264439105987550.8361737728118896 |
| AW | target_50TARGET | NUM | 0 | 3,292 | [-1.877361536026001, 2.3940649032592773] μ=-0.0 σ=1.0 | 0.68192732334136960.73543161153793330.7889358997344971 |
| AX | target_51TARGET | NUM | 0 | 3,322 | [-1.8843233585357666, 2.2996060848236084] μ=-0.0 σ=1.0 | 0.6921198368072510.74570274353027340.7992855906486511 |
| AY | target_6TARGET | NUM | 0 | 1,664 | [-1.784863829612732, 3.5898985862731934] μ=-0.0 σ=1.0 | 0.80328267812728880.85692304372787480.9105633497238159 |
| AZ | target_7TARGET | NUM | 0 | 3,226 | [-1.866620659828186, 2.451920986175537] μ=0.0 σ=1.0 | 1.64597237110137941.69634413719177251.7467159032821655 |
| BA | target_8TARGET | NUM | 0 | 202 | [-1.9097874164581299, 51.94083786010742] μ=-0.0 σ=1.0 | -0.255011737346649170.07946420460939407-0.5542796850204468 |
| BB | target_9TARGET | NUM | 0 | 3,513 | [-1.862041711807251, 2.5202383995056152] μ=0.0 σ=1.0 | 0.97418284416198731.02786529064178471.0815478563308716 |
| A | id | CAT | 0 | 1 | — | 619619619 |
| B | timestamp | CAT | 0 | 16,384 | — | 2024-06-12T09:05:002024-06-12T09:06:002024-06-12T09:07:00 |
| BC | type | CAT | 0 | 1 | — | ggg |
| BD | Application Usage | NUM | 0 | 1 | [1, 1] μ=1 σ=0 | 111 |
| BE | Infrastructure | NUM | 0 | 1 | [0, 0] μ=0 σ=0 | 000 |
| BF | Database | NUM | 0 | 1 | [0, 0] μ=0 σ=0 | 000 |
| BG | Networking | NUM | 0 | 1 | [0, 0] μ=0 σ=0 | 000 |
| BH | Security | NUM | 0 | 1 | [0, 0] μ=0 σ=0 | 000 |
FREQUENTLY ASKED QUESTIONS
Frequently Asked Questions: Boomlet Smart Meter Building Energy
Common questions regarding the Boomlet Smart Meter Building Energy dataset, machine learning task formulations, and in-browser tabular inference.
What is the forecasting horizon for Boomlet Smart Meter Building Energy?
The standard benchmark evaluates a 60-step forecast horizon evaluated across 20 rolling windows.
Can I forecast Boomlet Smart Meter Building Energy in Google Sheets?
Yes, Carla enables zero-server in-context time series forecasting directly in spreadsheets using foundation models.
LIVE EVALUATION IN GOOGLE SHEETS
Test TabICLv2 on Boomlet Smart Meter Building Energy Yourself
Open the pre-loaded Google Sheet and let Carla configure the target and task for local, zero-cloud tabular machine learning.
Step 1
Launch Carla
Click once to open the spreadsheet and Carla side panel together.
Step 2
Review the Setup
Carla selects the dataset target and task from this page automatically.
Step 3
Evaluate & Predict
Run predictions and compute metrics with zero server uploads.
Provenance & Attribution
Miller et al., The Building Data Genome 2 Project: An open dataset of electrical meter readings for 3,053 non-residential buildings, Scientific Data 2020.