Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model
Bibliographic record
Abstract
This paper investigates the relationship between a building's past energy consumption and the outdoor temperature and predicts the next day's energy consumption using a refined time series model. Maintaining optimal indoor temperatures relative to outdoor temperatures determines a building's HVAC demand and, thus, energy consumption. We want to determine how outdoor temperature and other factors determine this consumption. With increasing urbanization and energy demand, it is important to understand building energy consumption, especially in terms of its impact on the environment. Previous research has shown the link between electricity consumption and external environmental factors and highlighted energy optimization's importance in urban structures. As cities become large energy consumers, studies point to the need to understand energy use patterns on a regional and temporal scale. For accurate energy forecasts, data becomes the linchpin. Time series—data points arranged in chronological intervals—are foundational in predictive modeling. Due to buildings' intricate electricity consumption patterns, traditional linear forecasting often falls short. Enter nonlinear regression models: These complex models are apt for mapping and predicting nonlinear data trends. Notwithstanding their advantages, they come with challenges, primarily the high-frequency data influx from smart meters and IoT devices. But their potential benefits - from cost savings to efficient energy management - are significant. In a world caught between urban expansion and ecological preservation, efficient energy management is crucial. Accurate energy forecasting, especially for buildings, combines technological advances, statistical acumen and environmental imperatives. Understanding building energy consumption using sophisticated nonlinear regression models is evolving from an academic goal to a global necessity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".