ANN Daily Peak Forecast for Peak Demand Charges Management
Bibliographic record
Abstract
Demand charges (DC) is one of the major utility charges that represents a considerable portion of the electricity bill, especially in the case of large electricity consumers. Predicting the monthly peak of the facility helps manage peak demand charges (PDC). Forecasting of the monthly peak demand value of the facility is required to manage the PDC component of the energy bill. The monthly peak of a given class-A facility is a very specific and unique problem that requires an individual forecasting module for each facility because each facility is unique in its pattern of operation, energy consumption, and load profile. This paper proposes a methodology based on artificial neural networks (ANN) to forecast the daily peak demand of a given class-A facility to help manage its PDC. Based on the market regulations of Ontario (Canada), and the use of battery storage systems (BSSs) and real data for a large class-A Canadian electricity consumer in Ontario, the simulation results demonstrate the effectiveness of the proposed forecasting module in minimizing the DC cost of the class-A electricity customer. Using real class-A electricity consumer demand data, we show that our algorithm module is more consistent from day to day and provides a solution to peak demand problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".