Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving †
Abstract
:1. Introduction
- Cost-effective system design: A cost-effective and high-performance smart IoT system is designed by removing and integrating redundant IoT sensors.
- AI-based energy prediction technology for energy efficiency: A data analysis and prediction technology that enables meaningful inference through correlation analysis of data acquired from different heterogeneous IoT sensors installed inside a smart home for energy efficiency.
2. Related Works
2.1. Machine Learning-Based Smart Home
2.2. Deep Learning-Based Smart Home
2.3. Merit of the Current Study
- Eliminating redundant sensors: Establishment of a cost-effective smart home IoT system by eliminating redundant sensors.
- Increased accuracy: Reduction in unnecessary sensors actually increases the accuracy of the AI model.
- Power demand response: Prediction of power demand through environmental information sensor data analysis in the home.
3. Methodology
3.1. Methodology
- ①
- Sensing: Sensing environmental information inside the home from IoT sensors.
- ②
- Data acquisition: Collecting data from IoT sensors to Gateway.
- ③
- Training: Storing collected data in a database and performing machine learning training by importing the stored data. The algorithms used are Decision Tree Regressor (dt), Random Forest Regressor (rf), Extra Trees Regressor (et), Gradient-Boosting Regressor (gb), Hist Gradient-Boosting Regressor (hgb), and Deep Neural Network (DNN).
- ④
- Data analysis: Outputting the collected and trained data to the monitoring panel for users to visually check. Users can check the collected environmental information and also check the predicted data. At this time, the predicted data are HVAC power data.
- ⑤
- Data forecast: Predicting in advance how much power loss there will be in the future through predicted HVAC power. Through this, users can prevent their electricity bills from rising due to progressive power taxes.
- ⑥
- Management: Performing control for energy saving through predicted HVAC power data.
3.2. Background
3.2.1. Decision Tree
3.2.2. Random Forest
3.2.3. Extra Trees
3.2.4. Gradient Boosting
3.2.5. Hist Gradient Boosting
3.2.6. Deep Neural Network (DNN)
3.2.7. Recurrent Neural Network (RNN)
3.2.8. Convolutional Neural Network (CNN)
4. Service Overview
5. Implementation
5.1. Data Acquisition
5.2. Data Relevance Analysis
5.3. Data Classification
5.4. Model Development
- Decision Tree Regressor (dt)
- Random Forest Regressor (rf)
- Extra Trees Regressor (et)
- Gradient-Boosting Regressor (gb)
- Hist Gradient-Boosting Regressor (hgb)
5.5. Scenarios
6. Conclusions
- Data-driven intelligent energy system: This proposed system provides an intelligent energy service based on data by installing inexpensive IoT devices in a smart home. It moves away from the existing schedule-based equipment control method and analyzes future energy usage based on AI-based predicted power energy data to compare with the present.
- High scalability of IoT system: This proposed system removes duplicate IoT devices installed in a smart home, resulting in a structure that can achieve greater efficiency in larger spaces than in smaller ones.
- High performance of model and cost-effective system construction: This system removes IoT sensors that have no correlation by collecting environmental data from IoT devices installed in a smart home and analyzing the correlation of the collected data. This can increase the performance of AI models and reduce the price of IoT systems.
Funding
Data Availability Statement
Conflicts of Interest
References
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Author(s) | Publication Date | Application Field | Similarity | ||||||
---|---|---|---|---|---|---|---|---|---|
AI | IoT | Smart Home | Energy Management | Health Care | Security | Cost-Effective System Design | |||
Machorro-Cano, et al. [8] | 2020 | √ | √ | √ | √ | 75% | |||
Wang, et al. [9] | 2020 | √ | √ | √ | 50% | ||||
Filipe, et al. [10] | 2021 | √ | √ | √ | 60% | ||||
Jmila, et al. [11] | 2022 | √ | √ | √ | √ | 30% | |||
Huang, et al. [12] | 2023 | √ | √ | √ | √ | 60% | |||
Kabir, et al. [13] | 2015 | √ | √ | √ | √ | 55% | |||
Lee, et al. [14] | 2019 | √ | √ | √ | √ | 60% | |||
Li, et al. [15] | 2020 | √ | √ | √ | √ | 40% | |||
Kasaraneni, et al. [16] | 2022 | √ | √ | √ | √ | √ | 40% | ||
Popa, et al. [17] | 2019 | √ | √ | √ | √ | 65% | |||
Dey, et al. [18] | 2017 | √ | √ | √ | √ | 70% | |||
Rahman, et al. [19] | 2019 | √ | √ | √ | √ | 75% | |||
Uddin, et al. [20] | 2017 | √ | √ | √ | √ | 50% | |||
Sundaravadivel, et al. [21] | 2018 | √ | √ | √ | √ | 40% | |||
Solatidehkordi, et al. [22] | 2023 | √ | √ | √ | √ | 65% | |||
Proposed System | √ | √ | √ | √ | √ | - |
Items | Characteristics | Uses | |
---|---|---|---|
Temperature and humidity sensor |
| Collecting indoor temperature/humidity data | |
Fine dust/ CO2 sensor |
| Collecting indoor CO2, fine dust data | |
Smart motion sensor |
| Indoor user movement detection | |
Smart submeter |
| Measure the room’s power utilization |
Index | CO2 (ppm) | Fine Dust (µg/m2) | Humidity (%) | Temperature (°C) | Light Power (W) | Fan Power (W) | Room Power (W) |
---|---|---|---|---|---|---|---|
Count | 721 | 721 | 721 | 721 | 721 | 721 | 721 |
Mean | 947.2059 | 5.836186 | 56.14233 | 25.46187 | 15.7639 | 11.62537 | 119.0838 |
Std | 420.4869 | 4.574005 | 2.96905 | 0.331475 | 8.86753 | 8.814615 | 37.96945 |
Min | 168.15 | 1.92 | 50.03 | 23.64 | 9.87 | 0.12 | 38.99 |
25% | 655.91 | 3.35 | 54.17 | 25.44 | 13.35 | 5.72 | 104.67 |
50% | 839.03 | 4.5 | 55.7 | 25.54 | 13.41 | 9.55 | 111.03 |
75% | 1186.46 | 6.26 | 57.62 | 25.6 | 13.49 | 14.95 | 119.76 |
Max | 2441.44 | 44.88 | 79.69 | 26.69 | 58.75 | 42.62 | 311.35 |
Range | Meaning |
---|---|
From −1.0 to −0.7 | Strong negative linear relationship |
From −0.7 to −0.3 | Distinct negative linear relationship |
From −0.3 to −0.1 | Weak negative linear relationship |
From −0.1 to +0.1 | A linear relationship that can be almost neglected |
From +0.1 to +0.3 | Weak positive linear relationship |
From +0.3 to +0.7 | Clear positive linear relationship |
From +0.7 to +1.0 | Strong positive linear relationship |
Classifications | S1 | S2 | S3 | S4 | S5 |
---|---|---|---|---|---|
RMSE | 45.83 | 43.43 | 43.48 | 28.68 | 31.25 |
Classifications | Sensor List |
---|---|
S1 | CO2, Fine Dust |
S2 | S1 + Humidity |
S3 | S2 + Temperature |
S4 | S3 + Light Power |
S5 | S4 + Fan Power |
Classifications | Machine Learning Model |
---|---|
dt | Decision Tree Regressor |
rf | Random Forest Regressor |
et | Extra Trees Regressor |
gb | Gradient-Boosting Regressor |
hgb | Hist Gradient-Boosting Regressor |
Classifications | dt | rf | et | gb | hgb | S4 |
---|---|---|---|---|---|---|
RMSE | 35.50 | 24.48 | 23.35 | 22.29 | 27.95 | 28.68 |
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© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Park, S. Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving. Buildings 2023, 13, 2397. https://doi.org/10.3390/buildings13092397
Park S. Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving. Buildings. 2023; 13(9):2397. https://doi.org/10.3390/buildings13092397
Chicago/Turabian StylePark, Sanguk. 2023. "Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving" Buildings 13, no. 9: 2397. https://doi.org/10.3390/buildings13092397