Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving
Problem
Smart homes collect redundant sensor data, and homeowners lack tools to predict HVAC power use and save energy.
Why it matters
HVAC energy use is a major household cost, and redundant IoT sensors make systems unnecessarily expensive.
What the AI did
Machine learning models analyzed correlations among seven sensor types to identify redundant sensors and predict HVAC power consumption from six environmental inputs.
Results
Gradient-boosting regressor performed best with RMSE of 22.29 for HVAC power prediction.
Impact
The model enabled power prediction and sensor-importance analysis, supporting cost-effective IoT design, per the abstract.
Record
| Venue | Buildings |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Sanguk Park |
| Citations | 14 (OpenAlex, at import time) |
| References | 25 |
| Model or method | gradient-boosting regressor (gb) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/buildings13092397 |
| Open access | gold |
| Keywords | smart home, IoT sensors, energy saving, gradient boosting, HVAC prediction, RMSE |
Importance rating
real but narrower advance
Household HVAC energy efficiency and cheaper IoT design; abstract gives 13% cost reduction but no scale of affected homes, so importance inferred as a real but narrow smart-home advance.
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