Smart Home Energy Visualizer: A Fusion of Data Analytics and Information Visualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While technology advancements are continuously improving, the energy efficiency of household appliances, energy consumption analysis, and providing feedback to consumers on this analysis remains a critical issue in ensuring the effectiveness of such improvements. Visual feedback is a promising technique for promoting energy conservation by applying demand response (DR) in smart home energy management systems (SHEMSs). In this article, we propose a smart home energy visualization (SHEV) system, an SHEMS that comprises three components: Appliance Profile Detector with XCorrelation (APDX) that monitors the activation of household appliances, operation modes identification using cycles clustering (OMICC) to identify the operation modes used, and the visualizer to represent the appliance usage-related information to the user using concentric circles representation (CCR). This visualization assists the consumer in applying DR by better understanding the appliance usage so that the consumer makes sense of the consumption, and hence, better decision-making in energy conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it