An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting
Bibliographic record
Abstract
This paper proposes a new online-calibrated time series based model with the application to the day-ahead natural gas demand (GD) forecasting. A double-stage parallel process is developed for creating the forecasting model. The two stages include analysis of the temperature-independent and temperature-dependent components of the GD. The former stage is executed by online processing of the historical GD information considering the intertemporal variation of the GD. The latter stage, however, is conducted by exploiting the features of the GD information correlated with the ambient temperature. The forecast of the temperature is incorporated into the GD forecasting model through a correlation-based function. The model can generate the day-ahead GD forecast with both the hourly and intrahourly resolutions without compromising the forecast accuracy. The model is calibrated online using the historical GD and temperature information to achieve a higher forecast accuracy. The practical challenges associated with the industrial application of the model are also discussed. The application of the proposed model is numerically examined using real-world GD and temperature data, and the results are comprehensively studied. The outcomes reveal the efficacy and feasibility of the proposed model under various cases.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".