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Use Case

Predicting Efficiency of Smart Home Devices

Optimizing home energy efficiency through data-driven analysis of energy consumption and environmental factors.

1Overview & Strategic Importance

Predicting Efficiency of Smart Home Devices
Classification Solution IoT Data

Problem Statement

The adoption of smart home devices is rising as users rely on them for convenience and energy savings. However, ensuring the efficiency and reliability of these devices remains a challenge. Factors like energy consumption, usage patterns, and device malfunctions significantly affect user satisfaction. Predicting device efficiency is critical for manufacturers to improve product performance and for users to optimize their energy management.

Required Solutions

  • Developing a classification model for device efficiency.
  • Identifying factors contributing to device inefficiency.
  • Providing actionable insights to optimize usage and maintenance.
  • Enhancing user satisfaction by reducing malfunctions.

Solution Objectives

  • Perform exploratory data analysis to identify usage trends.
  • Build a machine learning model to predict efficiency status.
  • Analyze scenarios to maximize home efficiency.
  • Optimize the model for real-world IoT application.

Understanding the Problem

IoT devices like thermostats and smart lights offer convenience but their efficiency is influenced by age and malfunctions.

Predictive models help identify inefficiencies early, enabling proactive upgrades and better user guidance for sustainable energy management.

2About the Data

Data Collection

This dataset aggregates smart home metrics including usage patterns, energy consumption rates, and malfunction incidents collected via device logs and energy monitoring systems.

Dataset Source

Major Parameters Description

Download Training Data
UserID

A unique identifier for each user.

DeviceType

The type of smart home device (e.g., Lights, Thermostat).

UsageHoursPerDay

Average hours per day the device is used.

EnergyConsumption

Daily energy consumption of the device (measured in kWh).

UserPreferences

User preference for device usage (0: Low, 1: High).

MalfunctionIncidents

Number of malfunction incidents reported for the device.

DeviceAgeMonths

Age of the device in months.

SmartHomeEfficiency

Efficiency status of the device (0: Inefficient, 1: Efficient).

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `home_efficiency_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

Upload UI

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, simply ask a question like:
  • What changes can improve smart home efficiency based on the patterns in this data?
  • Which factors in the data have the biggest impact on smart home efficiency?

Operation Using Autonomous Guided Mode

AQuery Response

Optimizing `UserPreferences` and reducing `EnergyConsumption` are key. Customizing device settings to align with personal habits can significantly increase efficiency ratings.

Auto Analysis

BAI Application

The dynamic system lets users test different scenarios by adjusting variables like energy consumption and see how it impacts efficiency without technical expertise.

Auto Application

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'SmartHomeEfficiency' column was selected as the target.

Target Selection

BSelecting Analysis Type

The analysis target is a categorical column. Hence, the 'Classification' analysis type is selected.

Analysis Type

CSelecting Model Group/Item

Model Group

DSelecting Features

Select relevant features from the device logs.

Feature Selection

ESelecting Training Level

The "Slow" training level with "High Performance" configuration ensures accurate analysis of IoT patterns.

Training Level

AI Modeling Details

Random Forest is considered the best ML model for predicting device efficiency with the highest accuracy.

Modeling Details

Training Analysis Details

APredicted Target

Predicted Target

BROC AUC

ROC AUC

CError Trend

Error Trend

DFeature Importance

Feature Importance

Finalize Models

Train until accuracy is optimal and then click 'Deploy'.

Finalize Models

4AI APPLICATION

Manual Model Building

Modify sliders for `UserPreferences`, `UsageHoursPerDay`, `EnergyConsumption`, and `DeviceAgeMonths` to see real-time impact on predicted efficiency.

Manual App

AI Application Demo

High values of UserPreferences and low values of EnergyConsumption generally yield to efficient smart home status.

Saving the Project

Save your project by clicking the icon at the bottom left corner of the textbox.

Saving

Sharing the Project

Share the application for single on-demand predictions once the analysis is saved.

Sharing

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