Estimation of temperature correlation with household electricity demand for forecasting application
Bibliographic record
Abstract
This paper presents a new methodology for house-hold electricity demand forecast using a hybrid method combining nonparametric model and time series analysis. The relationship between outdoor temperature and electricity demand is studied in order to develop an approach to modeling and analyzing the electricity demand in response to temperature changes. First, the kernel density estimation as a nonparametric method is used to examine the mentioned relationship and provide probability distribution of future possible demand values. Second, two autoregressive (AR) models are applied to forecast total power demand using different information sources. These sources comprise results of forecasting temperature/electricity demand relationship as well as nonparametric residual data. The performance of the methodology represented in this work is evaluated using a comparison to the results of an ARMAX model. The forecasting accuracy of the models is compared on the basis of mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics. The forecasting results demonstrate that the hybrid model performs remarkably well and thus is more favorable than ARMAX model. This study employs real data for numerical analysis of proposed methods to increase the results efficacy.
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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".