Mining and Forecasting Energy Consumption Based on Weather Data
Bibliographic record
Abstract
For a modern grid to be reliable, energy efficiency and identifying consistent energy consumption patterns are becoming essential. In this paper, we present a data science solution that analyzes and predicts temporal energy consumption patterns using techniques like frequent pattern mining, traditional machine learning and deep learning. Specifically, our data science solution mines and forecasts energy consumption based on some meteorological and environmental conditions over time series (e.g., hourly or daily data), and examines how weather conditions affect energy usage variation. Evaluation results on a real-world dataset show that our data science solution identified several distinct frequent patterns when using frequent pattern mining with equally distributed bins. These patterns reveal a significant relationship between irradiance and energy consumption, as well as a positive correlation between temperature and energy usage. Furthermore, our solution predicts and compares energy consumption for a specific year using hourly and daily weather data with decision tree, gradient boosting, linear regression, and random forest. Additionally, we applied a long short-term memory (LSTM) model to view energy consumption as time-series data, uncovering patterns in the energy data based on given time steps. These results demonstrate the practicality of our data solution in mining and forecasting energy consumption based on weather data.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".