Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA
Why this work is in the frame
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Bibliographic record
Abstract
Rapid expansion of smart metering technologies has enabled large-scale collection of electricity consumption data and created the foundation for sensor-based load forecasting on individual buildings or even the household level. With continuously growing energy consumption, the importance of energy management including load forecasting is increasing in order to remedy the energy effect on the environment. Numerous machine learning techniques have been proposed for sensor-based load forecasting but most are offline approaches: the model is trained once and then used to infer future consumption. However, these approaches are not able to adapt to concept drift: for example, their accuracy will degrade when the building use changes or new equipment is installed. Thus, an approach capable of learning from new data as they arrive is needed. This paper proposes adaptive online ensemble learning with Recurrent Neural Network (RNN) and ARIMA for load forecasting under concept drift. The RNN part of the ensembles consists of Online Adaptive RNN as its underlying RNN learner has the ability to model temporal dependencies present in load data while its online nature enables continuous learning from arriving data. The adaptation to the concept drift is improved by adding Rolling ARIMA to the ensemble. The performance of the proposed approach has been examined on the four individual homes with different degrees of concept drift. The results show that the proposed ensemble achieves better accuracy than its constituent algorithms alone and, moreover, the analysis demonstrates the need to examine load forecasting approaches in respect to how they handle concept drift.
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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