LSTM-based Short-term Load Forecasting for Building Electricity Consumption
Why this work is in the frame
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Bibliographic record
Abstract
The unprecedented level of flexibility in energy management is required to ensure the balance of real-time energy production and consumption. Accurate short-term load forecasting (STLF) is vital for making the intelligent operation scheme. However, conventional forecasting techniques may not meet the increasingly demanding precision in load forecasting. This paper presents a novel energy load forecasting methodology based on Recurrent Neural Network (RNN), specifically Long Short-term Memory (LSTM) algorithms. The proposed LSTM-based model was trained and tested on a benchmark dataset which contained electricity consumption data for different kinds of buildings in America with onehour resolution. The comparative models including multi-layer perceptron neural network (MLP), random forest (RF), and kernelized support vector machine (SVM) was also tested on the same dataset. The week-ahead forecasting results have shown that the proposed LSTM-based model outdoes the three comparative models in nine of twelve months.
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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 it