Benchmarking and Evaluating Time Series Databases for Appliance-Level Energy Consumption Data
Bibliographic record
Abstract
Time series databases (TSDBs) are widely used to store high-frequency energy consumption data, but their performance varies depending on workload characteristics. This paper benchmarks leading TSDBs to identify their suitability to handle appliance-level, per-minute energy data. While prior work has evaluated TSDBs, limited research has been done on TSDBs for wide-format, fine-grained residential energy data at the appliance level. We introduce a custom-generated dataset simulating the usage of twenty-six appliances across five household types in a wide-format schema. Building on the TSM-Bench framework, we adapt it to support our appliance-level dataset, domain-specific workloads, and evaluation metrics. We analyze ingestion and query performance across three TSDBs that support this wideformat, while highlighting the trade-offs in latency, resource usage, throughput and storage. Looking ahead, we plan to evaluate schema transformations (wide to narrow), explore additional TSDBs optimized for narrow-format ingestion and compare their performance against wide-format results. These measures aim to provide a comprehensive and format-aware comparison of TSDBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".