An ensemble deep learning model for short-term load forecasting based on ARIMA and LSTM
Bibliographic record
Abstract
Electrical load forecasting is an important part of power system planning and operation, which can guide the power enterprises to arrange generation plan reasonably, reduce the cost of power generation, and provide a reference for power grid reconstruction and optimization. However, due to the complicated inner non-linear property and seasonality pattern of electrical load, accurate short-term load forecasting (STLF) is of big challenge. In this paper, we firstly study the large time-span quasi-periodicity of load sequences, including the inner correlation of a short load segment and the quasi-periodicity among the load segments spanning different time duration from a week to a month. Then, an ensemble method is proposed, which combines Auto-regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) in order to fully exploit the large time-span quasi-periodicity of the loads. Here, ARIMA model captures the stationary pattern of the load segments, while LSTM extracts the complicated non-linear relations of load segments. The proposed method is evaluated on a data set of load consumption in Toronto, and the results show the proposed method outperforms the existing popular STLF models with a small payload of computational complexity.
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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".