Using Regression Analysis for Predicting Energy Consumption in Dubai Police
Bibliographic record
Abstract
The aim of this project is to build a machine learning algorithm to forecast electricity and water consumption for the 27 sites in Dubai Police facilities. This aim is to establish a central database with all the data to monitor the energy consumption in a systematic manner and feed the data in a visualized dashboard. The data was collected from the energy conservation department at Dubai Police for five years from 2017 to 2021 comprising of electricity and water consumption details data. Due to the numerous buildings and facilities any irregular behavior in consumption takes time to be identified using conventional analysis methods, therefore this project will be able to support the organization to find out their energy savings/loss hotspots and facilitate immediate action for the employees to avoid time and monetary loss. Consumption data gathered will be processed through R Programming language to break it down into quarter consumption for each site. The Processed data for 2017 to 2020 will be an input for the multiple regression and ARIMA models to forecast the quarter consumption of year 2021 and to showcase the model. Finally, tableau software will be used to visualize the data and to build the dashboard in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".