Machine Learning in Smart Home Systems

Smart homes are defined with high needs of energy efficiency in home automation to which the IoT technologies are combined with Artificial Intelligence (AI) and the cloud computing system. That is why this research has posed the prospect of a new framework focused on the application of Artificial Intelligence and cloud solutions to manage energy usage in smart homes. The system harnesses data from smart IoT applications, statistical research and self-adjusting control to minimize energy consumption while providing comfort and satisfaction to the user. Based on initial experiments carried out in a smart home test bed, the proposed approach achieved peak hour energy savings of up to 20%. Due to the AI model's prediction accuracy, which was within 5% of actual usage, one could effectively manage smart devices. Furthermore, the cloudbase architecture also empowered the system's scalability and adequate time consumption with the response time below 0. 10 seconds for value added tasks and 5 seconds for critical tasks. The proposed strategy allows improving energy efficiency with further transition to smart home concepts while being applied as a long-term decision.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Artificial intelligence (AI) has received immense attention from the research community and the industry, leading to AI being adopted in many real-world applications. The growing trend of deploying AI has dramatically changed the ergonomics of modern-day practices in many realms, including smart homes, healthcare, insurance, investment and banking, social services, infrastructure, and marketing. A smart home, often referred to as an intelligent home, comprises smart technologies supported by AI. Smart home has its applications in household appliances, home safety and security, lighting and entertainment. Key industries have started integrating artificial intelligence with smart devices to enable connectivity among these devices. Smart meters could capture utility usage and track indoor temperatures and then deploy that information for action, which is where AI step in. The presented conceptual model will significantly facilitate future research regarding smart homes in the context of energy efficiency. The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. I.

Universal Wiser Publisher, 2025

Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.

We implement a basic solution for accumulating and obtaining user data within the existing home automation system in this paper. Data collection modules that operate on the home automation gateway and within the home automation cloud are implemented, allowing us to connect to the already existing big data middleware platform. It's the first step toward constructing the extensive data storage and analysis component for the existing housing automation solution. The collected data can be used to enable various scenarios of interest to end-users, such as the detection of emergencies and system irregularities.

The challenges of the Internet of Things (IoT) in a Smart Home are the monitoring of energy consumption and the automation of the household appliances connected to the Wireless Sensor Networks (WSN). The Smart Home monitoring technology has recently received great attention in the areas of IoT, home automation and WSN monitoring. Many companies seek to address a wide range of important issues including data mining and analysis, energy saving, comfort and security. The Smart Home application is inherently dynamic in the sense that it forms time-series data with real-time changing behavior. We seek to extract and analyze this incoming data to provide and predict useful features for the decision-making system in a Smart Home. This paper describes a new methodology of Smart Home data mining analysis based on Support Vector Machine (SVM) learning for a proposed Multi-Agent System (MAS). The key ideas are to represent the WSN behavior exchange by the modeled MAS, then to predict and class...

2020 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE), 2020

Artificial intelligence (AI) has received immense attention from the research community and the industry, leading to AI being adopted in many real-world applications. The growing trend of deploying AI has dramatically changed the ergonomics of modern-day practices in many realms, including smart homes, healthcare, insurance, investment and banking, social services, infrastructure, and marketing. A smart home, often referred to as an intelligent home, comprises smart technologies supported by AI. In this context, this paper presents a comprehensive overview of the existing state of the art on AI applications in the context of energy-efficient smart homes. This paper also proposes a conceptual model for AI-driven smart home aiming to enhance the users’ comfort and energy efficiency phenomenon. Moreover, a brief overview of different AI techniques employed in energy efficiency applications is also part of this work. The presented conceptual model will significantly facilitate future re...

IRJET, 2021

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Buildings are currently responsible for more than 40% of global energy and one third of global greenhouse gas emissions. This project work aims to develop a machine learning model, method, architecture or appliance to reduce building energy use and emissions using a smart sensor for residential or commercial buildings. An experienced operator can do a good job of adjusting set points and schedule. But no matter how good they are, a human's ability is limited by the amount of data he or she can process. Significant opportunities exist to take advantage of external data sources including real-time occupancy sensor networks, changing space schedules, weather forecasts, grid carbon intensity, and other environmental conditions that could help us better predict space set-points and schedules 24x7. The sensor senses CO2 reading, sound level, ambient light, door state sensing etc. These can be used to accurately estimate the number of occupants in each room using machine learning techniques and this technique can be used to predict future occupancy. In this project we take a look at some questions regarding the construction and the exploitation of knowledge related to different types of buildings in order to optimize the use of different resources while still assuring the occupants' comfort. The Heating, ventilation and Air conditioning (HVAC) equipments data of the building are captured using IOT sensors and the vast data collected using these sensors are analyzed using Machine Learning based Big data analysis technique and the decision obtained is used to control HVAC devices automatically. We design an IOT based Big data analysis model that characterize a building as smart and finally, it is planned to make Electrical Department of our Institution smart with our SB model.

2020

Thanks to Building Energy and Comfort Managements (BECM) systems, it is possible monitor and control buildings with the aim to ease appliance management and at the same time ensuring efficient use of them from the energetic point of view. To develop such kind of systems, it is necessary to monitor users’ habits, learning their preferences and predicting their sequences of performed activities and appliance usage during the day. To this aim, in this paper a system capable of controlling home appliances according to user preferences and trying to reduce energy consumption is proposed. The main objective of the system is to learn users’ daily behaviour and to be able to predict their future activities basing on statistical data about the activities they usually perform. The system can then execute a scheduling algorithm of the appliances based on the expected energy consumption and user annoyance related with shifting the appliance starting time from their preferred one. Experimental r...

International Journal of Smart Home, 2015

Context-awareness is the key element for building a smart home environment. The goal of a smart home is to predict the demand of home users and proactively provides the proper services by considering the user's context information. Several methods are used in context-aware system to provide services. Machine learning based approaches are capable to make better prediction and adaptation than others. In this paper, we present machine learning based context-aware system which can provide service according to the trained model. Two effective learning algorithms: Back propagation Neural Network, and Temporal Differential (TD) class of reinforcement learning are used for prediction and adaptation respectively. This approach indicates better adaptation for context-aware service due to the low error rate.

This paper aims at presenting the Smart Home concept. This paper describes in detail-a) The Smart Home concept b) Our concepts to model the Smart Home using smart devices c) Adaptive decision making using artificial intelligence and big data d) Large scale implementation of this concept to model a Smart Locality, Smart City up to the level of Smart country. Contrary to the other projects, this work is directed towards a sensors approach and an ontology modelling of the Smart Home. This work has the originality to take into account the real heterogeneity of information present in a habitat. This paper is a good overview to present what is a Smart Home and which are the necessary hardware and software components to make a Smart Home. Smart Home concept has been implemented using smart devices, adaptive decision making using artificial intelligence and big data. The work is directed towards a sensor approach and ontology modelling. This work focuses towards large scale implementation for smart systems.