Short-Term Electricity Load Forecasting and Seasonality Analysis Using Temperature and Artificial Intelligence Methods in the Southeastern Anatolia Region
Bibliographic record
Abstract
The planning of electrical energy systems can be realized in a more efficient and sustainable way by forecasting energy demand accurately. In this context, short-term load forecasting plays a critical role in optimizing energy production and distribution processes. In this study, short-term load forecasting was conducted using hourly electricity consumption data from a facility located in the Southeastern Anatolia Region between 2019–2022. The data were integrated with meteorological parameters to evaluate the impact of temperature. The performance of Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and AutoRegressive Integrated Moving Average (ARIMA) methods were compared. According to the results, the ARIMA method was the most successful with an accuracy rate of 92%, followed by the ANN model with 90% accuracy. The MLR method demonstrated relatively lower performance, achieving an accuracy rate of 89%. Moreover, ANN showed a strong capability to model complex relationships, while ARIMA excelled in datasets with seasonality. In conclusion, this study highlights the strengths and weaknesses of different methods, providing valuable contributions to energy planning and emphasizing the importance of analyses conducted using regional datasets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".