Description:FIELD OF INVENTION
The present invention relates to the field of smart home automation systems, particularly focusing on energy-efficient technologies. It involves the integration of context-aware artificial intelligence (AI) with Internet of Things (IoT) devices to optimize energy usage based on user behavior and environmental conditions. This invention aims to enhance sustainability, comfort, and cost-efficiency in residential settings.
BACKGROUND OF THE INVENTION
In recent years, the global surge in energy consumption has raised significant concerns about sustainability, environmental degradation, and rising energy costs. Traditional home automation systems, though offering convenience, often fail to dynamically adjust to the user's changing behaviors or environmental factors. As a result, energy is frequently wasted due to inefficient control of lighting, heating, cooling, and appliances, even when not required. This leads to increased carbon footprints and higher electricity bills, highlighting the need for a smarter and more adaptive solution.
Existing smart home solutions typically rely on pre-set schedules or user inputs, which lack real-time contextual awareness. They do not learn or adapt to user habits, leading to sub-optimal performance in terms of energy conservation. Moreover, these systems are often fragmented, with limited integration between devices and no central intelligence capable of making autonomous decisions. The absence of predictive analytics and machine learning capabilities restricts these systems from proactively optimizing energy usage based on external factors like weather conditions or occupancy patterns.
To address these challenges, the present invention proposes an energy-efficient smart home automation system powered by context-aware AI. By collecting real-time data from IoT sensors and analyzing it through AI algorithms, the system intelligently understands user behavior and environmental context. It autonomously manages home energy consumption by dynamically adjusting lighting, temperature, and appliance usage. This adaptive and proactive approach not only conserves energy and reduces operational costs but also contributes to a more sustainable and eco-friendly living environment.
OBJECTS OF THE INVENTION
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative
An object of the present disclosure is to enhances user comfort by dynamically adapting to personal habits.
Another object of the present disclosure is to reduces overall household energy consumption through intelligent automation.
Still another object of the present disclosure is to lowers electricity bills by minimizing unnecessary energy usage.
Another object of the present disclosure is to integrates renewable energy sources for sustainable living.
Still another object of the present disclosure is to enables real-time monitoring and control via mobile or voice interface.
Still another object of the present disclosure is to learns continuously to improve energy efficiency over time.
Yet another object of the present disclosure is to provides autonomous operation with minimal manual intervention.
Yet another object of the present disclosure is to ensures data privacy and system security through encrypted communication.

Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

The present invention is generally a smart home automation system powered by context-aware AI to optimize energy usage dynamically. It adapts to user behavior, environmental conditions, and real-time data for efficient energy control.
An embodiment of the present invention is IoT sensors are deployed throughout the home to collect real-time data such as occupancy, temperature, light levels, and appliance usage. This data is transmitted to a central AI unit for intelligent analysis and decision-making.
Another embodiment of the invention the AI unit utilizes machine learning algorithms to identify user habits and predict future energy requirements. This enables the system to proactively manage lighting, HVAC, and other appliances.
Yet another embodiment of the invention is the system includes a user-friendly interface accessible via mobile devices or voice assistants. It allows users to monitor consumption, give feedback, and override automation when necessary.
Yet another embodiment of the invention is renewable energy integration is supported, allowing the system to schedule energy-intensive tasks during periods of solar or wind power availability. This promotes sustainable energy use.
Yet another embodiment of the invention is continuous learning is built into the system, where AI improves its responses based on feedback and environmental changes. This ensures long-term optimization and personalized energy management.
Yet another embodiment of the invention is enhanced security features such as data encryption and role-based access controls are implemented. This protects user privacy while delivering smart, secure, and sustainable automation.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig 1: Block diagram of the overall architecture of the smart home automation system.
DETAILED DESCRIPTION OF THE INVENTION

The following description is of exemplary embodiments only and is not intended to limit the scope, applicability or configuration of the invention in any way. Rather, the following description provides a convenient illustration for implementing exemplary embodiments of the invention. Various changes to the described embodiments may be made in the function and arrangement of the elements described without departing from the scope of the invention.

The present invention relates to an “Energy-Efficient Smart Home Automation using Context-Aware AI”, provides an intelligent automation framework that optimizes energy consumption within residential buildings. It incorporates IoT sensors, machine learning models, and real-time context analysis to control appliances, lighting, heating, and cooling systems autonomously. This invention ensures that energy is used only when and where it is needed, based on continuous monitoring of user activity, environmental factors, and historical data patterns.

Figure 1 illustrates the overall architecture of the system, which consists of IoT-based sensors, a central AI processing unit, connected smart devices (lights, thermostats, appliances), and a feedback interface for user interaction. The sensors continuously gather data related to room occupancy, ambient temperature, humidity, lighting levels, and time of day. This raw data is transmitted securely to the AI processor using wireless communication protocols like Zigbee or Wi-Fi.

The AI decision-making module, which includes a data preprocessing unit, a context analyzer, a prediction engine, and an action generator. The context analyzer uses rule-based and probabilistic models to understand the situation — for example, distinguishing between an occupied room during the day versus nighttime sleep. The prediction engine employs machine learning algorithms (e.g., random forest, neural networks) to forecast user activity and energy requirements based on prior behavior and trends.

The invention includes an energy optimization module. Once a decision is made, the action generator sends appropriate commands to connected devices. For instance, if the system predicts no occupancy in a room for the next hour, it turns off the air conditioning and dims or turns off lights automatically. Similarly, it can pre-cool a room based on predicted arrival time or adjust appliance operation during off-peak hours to reduce energy costs.

The working of the invention is best explained through its operational flow. Initially, the system collects real-time data from all active sensors installed in various parts of the home. The AI then analyzes this data to detect user presence, preferences, and environmental conditions. Based on these insights, the system makes context-aware decisions on energy consumption. Actions are executed to optimize power usage through device adjustments. The system also incorporates continuous learning, updating its behavior over time based on user feedback and new data patterns.

The invention also includes a user feedback module accessible via a mobile application or smart assistance. This allows users to override decisions, view consumption analytics, and provide inputs to enhance AI learning. This ensures user control while maintaining the system’s autonomy. The interface displays insights such as real-time energy use, savings achieved, and suggestions for further optimization.

To support renewable energy integration, the system can also coordinate with home solar or wind power sources. For example, during peak sunlight hours, it can schedule high-energy tasks such as washing or cooling, ensuring maximum use of renewable energy. The system evaluates power availability from these sources before taking decisions, further enhancing sustainability.
Security and privacy are also integral to this invention. All data transmission is encrypted, and the AI module functions within a secure local or cloud-based environment. Role-based access controls and anonymized data processing protect user identities and personal routines from unauthorized exposure.

Additionally, the invention provides real-time energy monitoring and cost analysis, enabling users to understand where and how energy is consumed and what savings are achieved by each action. This fosters energy-conscious behavior and accountability among users. The data generated can also help utility providers design dynamic tariff plans or encourage load balancing.

In conclusion, this invention transforms a standard smart home into a context-aware energy-efficient ecosystem. It blends user comfort, environmental consciousness, and cost-effectiveness using advanced AI and IoT technologies. By enabling proactive, autonomous, and adaptive energy management, it significantly advances the current state of home automation systems.

The operation of the energy-efficient smart home automation system begins with the continuous collection of real-time data from various IoT sensors installed throughout the home, which monitor occupancy, temperature, humidity, lighting conditions, appliance usage, and external factors like weather. This data is transmitted to a central AI processing unit that performs context analysis to understand user behavior, daily routines, and environmental settings. The AI employs machine learning algorithms to predict future activity patterns and determine optimal energy usage strategies. Based on these insights, the system autonomously adjusts lighting, heating, cooling, and appliance operations — for example, dimming lights or switching off devices in unoccupied rooms and pre-adjusting room temperature prior to user arrival. The system also considers renewable energy inputs, like solar power, for task scheduling during peak generation hours. A user interface, available via mobile app or smart assistant, allows users to view energy usage, provide feedback, and override system decisions when needed. The AI continuously learns from these interactions and evolving data to refine future actions, ensuring maximum energy efficiency, comfort, and cost savings while maintaining user preferences and privacy.
While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:We Claim,
1. An energy-efficient smart home automation system using context-aware artificial intelligence (AI), comprising:
a plurality of Internet of Things (IoT) sensors configured to collect real-time data on environmental conditions, user occupancy, and appliance usage within a residential space;
a central AI processing unit operatively connected to said IoT sensors and configured to analyze contextual data using machine learning algorithms to detect patterns of user behavior and predict energy requirements;
a decision-making module within the AI processing unit configured to generate control commands based on the analyzed data and predicted context;
a plurality of smart devices including lighting systems, HVAC (heating, ventilation, and air conditioning), and household appliances configured to receive said control commands and autonomously adjust operational states for optimal energy consumption;
a user interface accessible via mobile or voice-controlled device, allowing manual overrides, preference setting, and feedback provision;
wherein said system is configured to continuously learn from user feedback and environmental changes to dynamically adapt future control decisions, thereby minimizing energy waste while maintaining user comfort.

2. The system as claimed in claim 1, wherein the AI processing unit uses supervised or unsupervised machine learning models selected from neural networks, random forest, or decision trees for behavior prediction and energy optimization.
3. The system as claimed in claim 1, wherein the IoT sensors are selected from the group consisting of motion detectors, temperature sensors, humidity sensors, light sensors, and smart meters.
4. The system as claimed in claim 1, wherein the AI decision-making module is further configured to schedule high-energy tasks during off-peak hours or when renewable energy sources are available.
5. The system as claimed in claim 1, wherein the user interface displays real-time energy consumption data, comparative analytics, and energy-saving recommendations.
6. The system as claimed in claim 1, wherein the system supports integration with renewable energy sources such as solar panels or wind turbines for adaptive load scheduling based on availability.
7. The system as claimed in claim 1, wherein data transmission between the IoT sensors and the AI processing unit is encrypted to ensure secure communication and privacy.
8. The system as claimed in claim 1, wherein the AI continuously updates its prediction model based on periodic user feedback and detected environmental changes to improve efficiency over time.
9. The system as claimed in claim 1, wherein the system generates notifications or alerts for anomalous energy usage or system faults to the user interface.
10. The system as claimed in claim 1, wherein the AI decision-making module is configured to manage multi-room environments individually based on localized sensor input and context analysis.

Dated this 18 June 2025

Dr. Amrish Chandra
Agent of the applicant
IN/PA No: 2959