LSTM-Based Deep Learning Long Term Electric Demand Prediction for Karnataka
Bibliographic record
Abstract
Long Short-Term Memory (LSTM) simulations offer a robust time series-based electric demand forecasting framework. By leveraging the memory capabilities of LSTM networks, accurate predictions can be made for future demand, aiding in efficient resource allocation. LSTM networks have emerged as powerful tools for time series forecasting. Their ability to capture long-range dependencies and handle temporal patterns makes them particularly effective in predicting future values of sequential data. The input data is required for the LSTM, the power demand of Karnataka state has been taken as a study model. It explores the application of LSTM models in time series forecasting, highlighting their strengths in capturing complex temporal dynamics and achieving accurate predictions across various domains. Historical demand data has been collected from 1975 to 2023. Electric demand is forecasted till 2029. The effectiveness of the proposed algorithm has captured long-range dependencies and outperformed traditional methods.
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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".