scholarly journals Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 481 ◽  
Author(s):  
Saad Jaouhari ◽  
Emilio Palacios-Garcia ◽  
Amjad Anvari-Moghaddam ◽  
Ahmed Bouabdallah
Keyword(s):  
Energy Consumption ◽  
Rural Areas ◽  
Smart Home ◽  
Smart Homes ◽  
Heterogeneous Data ◽  
Comfort Level ◽  
Medical Services ◽  
Next Generation ◽  
Energy Devices ◽  
Home Energy Management

This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

scholarly journals A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 108 ◽  
Author(s):  
Abdul Shah ◽  
Haidawati Nasir ◽  
Muhammad Fayaz ◽  
Adidah Lajis ◽  
Asadullah Shah
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Optimization Algorithms ◽  
Energy Optimization ◽  
Smart Homes ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Comfort Level ◽  
Energy Consumption Optimization ◽  
User Comfort

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

scholarly journals HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1097 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Smart Home ◽  
User Preferences ◽  
The Internet ◽  
Automation Systems ◽  
Home Energy Management

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

scholarly journals A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3450
Author(s):  
Muhammad Diyan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Smart Home ◽  
Scheduling Algorithm ◽  
Smart Homes ◽  
Human Interaction ◽  
Household Appliances ◽  
Slack Time ◽  
Home User

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

Smart Home Energy Management with Electric Vehicles Considering Battery Degradation

2013 ◽  
Vol 860-863 ◽  
pp. 1085-1091 ◽  
Author(s):  
Qi Zhang ◽  
Shao Hua Zhang
Keyword(s):  
Electric Vehicles ◽  
Smart Home ◽  
Comfort Level ◽  
Storage Device ◽  
Swarm Optimization ◽  
Battery Lifetime ◽  
Optimal Dispatch ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Battery Degradation

With the development of vehicles-to-grid (V2G) technology, electric vehicles (EVs) are receiving increasing attention in recent years. This paper describes a smart home energy dispatch model including EVs and flexible appliances. In this model, the home's electricity cost is minimized through optimal dispatch, while the comfort level of home is considered. A simplified method to measure battery degradation is proposed. The model is solved by the dissipative particle swarm optimization (DPSO) algorithm. Simulation results show that the battery lifetime can be extended when the battery degradation is considered in the charging and discharging of the EV. With the decrease in the battery cost, the user has more incentives to use the EV as a storage device to reduce the electricity cost.

Energy Information Communication Technologies for Smart Home Applications

2013 ◽  
Vol 302 ◽  
pp. 679-685
Author(s):  
Huo Ching Sun ◽  
Yann Chang Huang ◽  
Hsing Feng Chen
Keyword(s):  
Smart Home ◽  
Effective Means ◽  
Smart Homes ◽  
Communication Technologies ◽  
Information Communication Technologies ◽  
Information Communication ◽  
Home Energy Management ◽  
Home Energy Management System ◽  
Historical Developments ◽  
Energy Information

This paper reviews previous and recent trends in energy information communication technologies (EICT) for smart home applications. Relevant EICT publications on smart homes are reviewed. Smart home and smart home energy management system (SHEMS) related concepts are described, followed by a thorough review of SHEMS and EICT technologies. As is increasingly recognized, EICT is a highly effective means of monitoring, controlling, and conserving energy consumption in smart home applications. Additionally, various EICT approaches are surveyed to evaluate the feasibility of smart home applications by discussing historical developments and introducing advanced EICT methods. Importantly, in addition to surveying the latest trends, this study contributes to efforts to further advanced EICT applications in smart homes.

scholarly journals Smart homes: potentials and challenges

Clean Energy ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 302-315
Author(s):  
Rasha El-Azab
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
Smart Home ◽  
Smart Homes ◽  
Clean Energy ◽  
Economic Benefits ◽  
Home Economic ◽  
High Penetration ◽  
Home Energy Management ◽  
Management Schemes

Abstract Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants. Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility. Smart homes have been introduced recently as an alternative solution to classical power-system problems, such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages. The appliances, sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme. Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle. The intermittent uncertain nature of smart homes may badly affect the whole grid performance. The prospective high penetration of smart homes on a smart power grid will introduce new, unusual scenarios in both generation and loading. In this paper, the main features and requirements of smart homes are defined. This review aims also to address recent proposed smart-home energy-management schemes. Moreover, smart-grid challenges with a high penetration of smart-home power are discussed.

Empower Saving Energy into Smart Communities using Social Products with a Gamification Structure for Tailored Human-Machine Interfaces within Smart Homes

2021 ◽  
Author(s):  
Juana Isabel Méndez ◽  
Pedro Ponce ◽  
Alan Meier ◽  
Therese Peffer ◽  
Omar Mata ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Behavioral Problems ◽  
Smart Home ◽  
Residential Buildings ◽  
Smart Homes ◽  
End Users ◽  
Usability Problems ◽  
Reduce Energy Consumption ◽  
Social Product ◽  
Smart Community

Abstract Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can profile the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human-Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gamification features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The first step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gamification elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gamification elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gamified mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

scholarly journals Environment Optimization Scheme Based on Edge Computing Using PSO for Efficient Thermal Comfort Control in Resident Space

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 241
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Dohyeun Kim
Keyword(s):  
Thermal Comfort ◽  
Home Environment ◽  
Smart Home ◽  
Smart Homes ◽  
Pso Algorithm ◽  
Edge Computing ◽  
Comfort Level ◽  
Optimization Scheme ◽  
Ongoing Research ◽  
Smart Home Environment

With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live experiences. The IoT and cloud-dependent smart home services related to recent researches have security, bandwidth issues, and a lack of concerning thermal comfort of residents. In this paper, we propose an environment optimization scheme based on edge computing using Particle Swarm Optimization (PSO) for efficient thermal comfort control in resident space to overcome the aforementioned limitations of researches on smart homes. The comfort level of a resident in a smart home is evaluated by Predicted Mean Vote (PMV) that represents the thermal response of occupants. The PSO algorithm combined with PMV to improve the accuracy of the optimization results for efficient thermal comfort control in a smart home environment. We integrate IoT with edge computing to upgrade the capabilities of IoT nodes in computing power, storage space, and reliable connectivity. We use EdgeX as an edge computing platform to develop a thermal comfort considering PMV-based optimization engine with a PSO algorithm to generate the resident’s friendly environment parameters and rules engine to detects the environmental change of the smart home in real-time to maintain the indoor environment thermal comfortable. For evaluating our proposed system that maintenance resident environment with thermal comfort index based on PSO optimization scheme in smart homes, we conduct the comparison between the real data with optimized data, and measure the execution times of optimization function. From the experimental results, when our proposed system is applied, it satisfies thermal comfort and consumes energy more stably.

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