A Data Science Solution to Integrate Weather Data for Energy Consumption Analysis
Bibliographic record
Abstract
Energy efficiency and identification of consistent energy consumption patterns are crucial for reliability of a modern power grid. In this paper, we present a data science solution that integrates weather data with historical energy consumption data for energy consumption analysis. Consequently, the solution predicts temporal energy consumption patterns via techniques like frequent pattern mining, traditional machine learning, and deep learning. Our solution integrates meteorological and environmental conditions over time series, analyzes them, forecasts energy consumption, and examines how weather conditions affect energy usage variation. Evaluation results on a real-world dataset show that our solution identifies several distinct frequent patterns with frequent pattern mining, and it reveals a significant relationship between irradiance and energy consumption, as well as a positive correlation between temperature and energy usage. Moreover, our solution predicts and compares energy consumption for a specific year using linear regression, decision tree, random forest, and gradient boosting models with daily weather data. Additionally, we applied a long short-term memory (LSTM) model to analyze energy consumption as time-series data, uncovering patterns based on given time steps. These results demonstrate the practicality of our data science solution for energy consumption analysis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".