Bibliographic record
Abstract
Load forecasting (LF) is of great significance for effective operation, utilization, safety and reliability of the modern electric power systems. Load forecasting can be categorized into very short term, short-term, medium-term, and long-term forecasts, depending on which time scale is concerned. The short term load forecasting (STLF) plays an increasingly important role in achieving a more efficient, reliable and safe power system. Its outputs are the indispensable inputs of generating scheduling, power system security assessment and power dispatch. In the era of smart grid (SG), STLF is the basic building block to imply Demand Side Management (DSM) in areas such as automatic generation control, load estimation, energy purchasing, and contract evaluation, etc. The accuracy of STLF is of essential importance for both economic and reliability. In the last few decades, various methods have been devised and applied to perform STLF. Due to its superior capability of handling the nonlinearity, Artificial Intelligence (AI) based techniques are gaining more popularity in a variety of applications. The objective of this study is to review, categorize, evaluate, and analyze the principle, application, and performance of STLF techniques. It builds up several feed forward Artificial Neural Networks (ANN) models with different configurations, and studies the mechanism of ANN for effective STLF. With 12 years of hourly load and meteorological data sets of a section of the City of Toronto, the configurations are built up with different hidden layers, activating function, training algorithms and both un-normalized and normalized data to predict the day ahead STLF with satisfactory result achieved.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".