Urban Electricity Consumption Forecasting Based on SARIMA and Random Forest Modeling
Bibliographic record
Abstract
This project proposes to use a combination of machine learning and time series analysis to provide in-depth analysis and forecasting of electricity consumption in a city in North Africa. The dataset used in this study contains a range of information including date, temperature, humidity, wind speed, total flow, and electricity consumption. The project proposes to reveal patterns and patterns of electricity consumption behavior through data preprocessing, normalization, and seasonal decomposition. The project proposes to use two models: Seasonal Autoregressive Integrated Sliding Average (SARIMA) and Random Forest based on feature engineering. The SARIMA method is used to analyze the seasonality and trend of the time series data, and the Random Forest method is used to study the nonlinear relationship between electricity consumption and environmental factors. On this basis, we add more information such as rolling rolling standard deviation, minimum large value, and time-delayed features to the random forest. This method greatly improves the prediction accuracy of power consumption. The experimental results show that compared with the single SARIMA model, the random forest model using j combined with the feature engineering method can better predict the load changes of the power system. The results show that the Random Forest model can capture the complexity of power consumption more effectively, especially after adding detailed feature items. At the same time, the good interpretability and flexibility of Random Forest makes the model able to better understand and predict the urban power demand, which can effectively help the power grid enterprises to realize the optimal allocation of resources and reduce energy consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".