Smart Home Energy Management System Research Articles

“It's never telling me that I'm good!” Household experiences of testing a smart home energy management system with a personal threshold on energy use in Sweden

Although smart technologies are widely promoted as enabling flexibility in households' energy demand, they often fail to achieve substantial impact. How smart technologies are actually used and to what extent they enable changes of energy-reliant practices in everyday life therefore needs to be better understood. This paper evaluates a smart home energy management system, Ero 2.0, which was tested by households in a multi-residential building in Sweden. To our knowledge, apart from its forerunner, Ero 2.0 is the first of its kind to include a personal threshold on energy use varying with the availability of preferred energy sources. Semi-structured interviews were conducted with 15 participants, complemented by pre and post surveys, answered by 39 and 32 participants respectively. Overall, Ero 2.0 contributed to raised awareness regarding electricity and water use. To some extent, it also contributed to changes in practices, mainly through changed or reduced use of certain functions. However, it was less effective in enabling shifting practices in time. Difficulties to change practices were found to depend on: (1) lack of flexibility in everyday life, (2) limitations in size and layout of the dwelling, (3) lack of incentives and perceived impact, (4) lack of guidance and (5) lack of possibilities to control devices through the interface. The second point is an aspect that to date has received little attention in smart energy research. Design opportunities for future smart home energy management systems are discussed while acknowledging that such technologies cannot alone achieve the transition to more sustainable energy systems.

A Transfer Reinforcement Learning Framework for Smart Home Energy Management Systems

Transfer learning (TL) is widely used as a solution to overcome the huge amount of time required to train a deep learning model. However, a number of challenges are associated with transferring the knowledge between the same and different target domains (TDs), for instance, designing efficient mapping functions, correlating the source and target datasets, identifying necessary parameters for enabling TL, and so on. In this article, we, therefore, present a solution to enable TL for home energy management systems (HEMSs) in smart homes consisting of multiple residents and appliances. This led to eliminating the training time and data requirements associated with training home appliances. A mapping function is designed based on correlating the source and target datasets before transferring the knowledge. Furthermore, a training model is designed to iteratively train a source model, i.e., expert home (EH) and expert appliance (EA), with source dataset and feedback knowledge from the target model, i.e., learner home (LH) and leaner appliance (LA). An extensive set of simulations is carried out to evaluate the performance of the proposed scheme. Simulation results show that transferring knowledge in both the same and different domains significantly reduces the energy consumption of individual home appliances and smart homes. In the future, we will expand the research using new models to promote TL effectiveness.

Design of type-2 fuzzy logic controller in a smart home energy management system with a combination of renewable energy and an electric vehicle

Home energy management is one of the most important parts of a smart home that manages the efficient use of energy in the smart home. This paper aims to design two type-2 fuzzy logic controllers in the demand-side energy management system. For this purpose, a combination of renewable energy sources, such as fuel cells, photovoltaic solar panels, vertical axis wind turbines with a helical savonius rotor, and an electric vehicle and energy storage system along with an external grid are utilized to supply the electricity usage of home appliances in a smart home. By using the proposed method, higher quality, more economical, and environmentally friendly energy can be accomplished. To this end, the outputs of the proposed controllers are defined to make appropriate decisions about the generated energy, how to supply energy for own consumption, and controllable loads. The number and distribution of membership functions of type-2 fuzzy logic controllers are selected according to the measured real input data over a year in Tehran, Iran. To demonstrate the effectiveness of the proposed method it is simulated by MATLAB/Simulink software, which indicates that the proposed system causes the smart home to receive 49.186 kWh less electricity energy from the grid to supply daily power for home appliances and therefore can weekly consume approximately 343.95 kWh less electricity energy from the grid. it is shown that after applying the proposed strategy, electricity costs were reduced by 71.5%, and the peak-to-average ratio was reduced by 64.6%.

Development of a Multi-Asset Risk Assessment Algorithm in the Context of Home Energy Management

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector’s energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.

Artificial Intelligence Based Smart Home Energy Management System

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning

The paper “Design and Implementation of a Smart Home Energy Management System Using IoT and Machine Learning” proposes a system that aims to optimize energy consumption in a smart home environment. The system uses Internet of Things (IoT) devices to collect real-time data on energy usage and machine learning algorithms to predict future consumption patterns. This paper proposes the use of deep neural networks (DNNs) for the design and implementation of a smart home energy management system using IoT and machine learning techniques. The authors demonstrate the effectiveness of the system through experimental results, showing significant energy savings compared to traditional methods. The DNN is built using Keras or Tensor Flow and is trained on extracted features from energy consumption data collected using IoT sensors. The system is implemented with a real-time monitoring system and a user interface for remote access. The proposed system has the potential to save energy and reduce energy costs for households while providing real-time feedback to the user.

Activity and efficiency of the building sector in Morocco: A review of status and measures in Ifrane

<abstract> <p>One-third of all greenhouse gas emissions come from the world's building stock while accounting for 40% of global energy use. There is no way to combat global warming or attain energy independence without addressing the inefficiency of the building sector. This sector is the second consumer of electricity after the industrial sector in Morocco and is ranked third emitter after the energy sector and transportation sector. Using Ifrane as a case study, this paper examines and reviews the city's energy use and the initiatives taken to improve building efficiency. The findings showed that, during the analyzed period, i.e., from 2014 to 2022, Ifrane's annual electricity consumption climbed steadily from 35 to 43 GWh. The government of Morocco has implemented effective laws, guidelines and regulations, as well as publicized ways to reduce energy consumption and increase energy efficiency. However, gathered data and survey results revealed opportunities and challenges for enhancing Ifrane's efficient energy use.</p> <p>The study also evaluates government programs, codes/standards and related actions for the improvement of household energy efficiency. As part of the review, the available literature was analyzed to assess the effectiveness of energy behavior and awareness, the impact of an economical and sustainable building envelope, the impact of building retrofitting programs, the importance of energy-performing devices and appliances, the adoption of smart home energy management systems, the integration of renewable energies for on-site clean energy generation and the role of policies and governance in the building sector in Ifrane. A benchmark evaluation and potential ideas are offered to guide energy policies and improve energy efficiency in Ifrane and other cities within the same climate zone.</p> </abstract>

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification.

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

Artificial Intelligence Based Smart Home Energy Management System: A Review

Recently, countries are faced with an unreliable electric power supply to homes and the industry due to insufficient supply from the utility, causing load shedding. With the power utilities struggling to keep the lights on, there are initiatives to invest in backup power solutions and home energy management systems (HEMS) to manage this development. This paper analyzes energy consumption with the existing infrastructure of typical equipment used in homes. An approach called smart home energy management systems (SHEMS) is implemented to balance the power demand and the supply more effectively throughout the smart micro-grid by utilizing control algorithms simulated using MATLAB Simulink. This is achieved by utilizing real data from a home with commercial and residential load features in an urban area. The SHEMS revealed greater management and efficiency in electric power savings by reducing the utilities power demand. The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed. Key Words: Balance of Power Demand, Energy efficiency, Smart Distribution Board (SDB), Smart Home Energy Management System (SHEMS), Artificial Intelligence.

Does electricity consumption make happy? The emotional dimensions of time-scaled electricity consumption graphs for household appliances

Due to the current situation regarding climate change, smart home energy management systems help to provide an overview of a household’s electricity consumption at appliance level by means of time-scaled visualizations. Such electricity consumption graphs need to be visually appealing to unleash potentials for a positive contribution to energy transition. Triggered emotions in particular play a crucial role for visualization design. Therefore, this paper presents an in-depth facial expression analysis and eye-tracking investigation with a sample of n = 48 non-expert participants with the aim to determine the triggered emotions by different time-scaled electricity consumption graphs at appliance level. Participants were randomly assigned to one of three groups with different time-scaled electricity consumption graphs at appliance level. Within each group, the participants had to complete three tasks based on the presented consumption graph. Results revealed that time-scaled electricity consumption graphs at appliance level have the potential to trigger the emotions joy, surprise, anger, contempt, disgust and sadness. Surprise, contempt and disgust in particular were triggered more frequently than anger and sadness. Furthermore, electricity consumption graphs at appliance level triggered joy more frequently among women than men. However, the type of electricity consumption graph did not influence the extent of triggered emotions. Nevertheless, smart home energy management system suppliers should consider this triggering effect on human emotions to provide adequate solutions that unleash the full potential to support the energy transition.

Energy Management System for Domestic Applications

The power grid is amid the period of transformation. Consumers are interested in taking on a dual role as producers and consumers, or prosumers, in the operation of the electric grid. Renewable energy sources are increasingly being used in the residential sector, which has environmental advantages but also presents issues in terms of energy management. Misleading patterns may result, for instance from the energy usage of common household equipment. Energy prices are a further obstacle because of the potential for economic loss to the user as a result of inefficient systems or imbalanced energy management. The Home Energy Management Systems (HEMS) are a recent innovation that aim to address this issue. When properly implemented, the Smart HEMS uses a Hidden Markov Model-based method to calculate the likelihood that a given state of the house really exists. The suggested system makes use of GSM technology for outside communication and WiFi for interior data transfer. Tests show that the suggested approach and algorithm are effective, and that they have reduced the energy use by 18%.

Energy optimization in smart urban buildings using bio-inspired ant colony optimization

In this paper, a smart home energy management system is proposed to improve the efficiency of the electricity infrastructure of residential buildings. To solve the scheduling problem of a smart building, we propose bacterial foraging ant colony optimization (HB-ACO). The primary objective of scheduling is to shift load from on-peak hours to off-peak hours to reduce electricity cost and peak-to-average ratio. A comparison of these algorithms is also presented in terms of performance parameters, electricity cost, reduction of PAR, and user comfort in terms of waiting time. The proposed techniques are evaluated using two pricing schemes: (1) time of use and (2) critical peak pricing. Moreover, coordination among home appliances is presented for real-time scheduling. We represent this as a knapsack problem and solve it through ant colony optimization algorithm. The HB-ACO shows better performance than ACO and BFA in reducing electricity cost, PAR, and increased user comfort, which is evident from the simulation results.

A Prosumer-Oriented, Interoperable, Modular and Secure Smart Home Energy Management System Architecture

As prices on renewable energy electricity generation and storage technologies decrease, previous standard home energy end-users are also becoming producers (prosumers). Together with the increase of Smart Home automation and the need to manage the energy-related interaction between home energy consumers and Smart Grid through different Demand Response approaches, home energy management becomes a complex and multi-faceted problem, calling for an extensible, interoperable and secure solution. This work proposes a modular architecture for building a Smart Home Energy Management System, integrable with existing Home Automation Systems, that considers the use of standard interfaces for data communication, the implementation of security measures for the integration of the different components, as well as the use of semantic web technologies to integrate knowledge and build on it. Our proposal is finally validated through implementation in one real smart home test-bed, evaluating the system from a functional standpoint to demonstrate its ability to support our goals.

Development and Application of Smart Home Energy Management System Based on Wireless Network Technology

In order to solve the problem of integration of home energy management system and wireless network technology, the author proposes the development and application of a smart home energy management system based on wireless network technology. Divide the NB-IoT into different simulation levels, simulate the physical layer of the NB-IoT, and according to the household energy conservation rules, calculate the household energy distribution parameters, convert the intelligent control signal into a signal constellation point, calculate the deviation value of the control command corresponding to the energy equipment, build an intelligent control simulation algorithm, and finally complete the simulation of the intelligent control system. Experimental results show the following: The simulation method in the intelligent home language input terminal control system is based on the Internet of Things, the average time of simulating the test point of energy equipment is about 4.9 s, the required simulation time is long, and in the intelligent control system of the mobile road tunnel lighting vehicle, the simulation time required by the designed simulation method is about 6.8 s, and the simulation time is the longest. The average time of the method proposed by the author to simulate the energy distribution instruction is about 3.3 s. Conclusion. The application prospect of a smart home energy management system based on wireless network technology is very broad.

New smart home energy management systems based on inclining block-rate pricing scheme

AbstractThere are wide applications of block-rate pricing schemes in many countries. However, there are no significant studies that apply this common tariff for smart home energy management systems. In this paper, a three-time-frame energy management scheme has been proposed for photovoltaic (PV)-powered grid-connected smart homes based on the well-known mixed-integer linear programming optimization technique. This paper provides three original and novel smart home energy management algorithms that depend on the most common residential tariff specifically in developing countries. Three different management concepts have been studied for a typical Egyptian house. The concepts of shifting load, vehicle-to-home and reducing air conditioning have been tested according to a commonly applied slab tariff. The proposed scheme considers the home battery extending lifetime constraints. It also preserves comfortable lifestyle limits for home users according to Arab housing climatic conditions and culture. Moreover, the economic feasibility of integrated PV modules for the studied home has been verified according to the Egyptian tariff. The proposed energy management scheme of PV-powered home reduces the electrical power bill significantly in a wide range from 61% to only 19% of the default case bill according to the applied management technique.

Optimal Home Energy Management Considering Uncertainties in Occupancy, Consumption, and Electricity Price

Power utilities issue demand response (DR) during the hours of peak load in order to reduce the demand on the network and provide congestion relief to overloaded circuits. While traditional residential DR programs are mainly one-way in the form of remote on/off control of air conditioning (A/C) units, residential customers can adopt a more proactive role through utilizing the capabilities of smart meters and home energy management systems (HEMS). HEMS can monitor energy rates and DR incentives, and accordingly change the temperature setpoint of the A/C unit and/or shift appliance loads from peak to off-peak hours in order to maximize financial benefits. All this can be achieved in an automated human-out-of-the-loop fashion. From the HEMS’ standpoint, the task can be viewed as solving an optimization problem with the goal of reducing power consumption while maximizing financial gains. However, another equally important goal would be to ensure that the comfort level of residents, if present in the building, is not compromised. This is especially crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents’ health, especially children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g., electricity rates, building occupancy levels, and demand. This paper presents a novel solution for energy management of a smart home using DR by considering the above factors. To ensure that the solution found is feasible against all possible uncertainties, a robust model is developed and solved for a given time horizon. As shown through simulation results, considering uncertainties are necessary, since they can change the solution in a nonnegligible way.

Residential energy management system based on integration of fuzzy logic and simulated annealing

With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios by consumer behavior. In this study, an intelligent residential energy management architecture and algorithm to manage residential energy production/consumption are proposed. The algorithm controls the energy flow in the home according to real-time potential solar power estimation, demanded energy estimation, electricity consumption price, and battery state-of-charge (SoC). The fuzzy logic algorithm has been developed to determine the estimated comfort and cost-effectiveness ratios in the near future. The simulated annealing algorithm, a meta-heuristic algorithm, is performed to obtain the best operating point decision of the battery using the comfort and cost-effectiveness ratios. Energy flow direction and battery SoC are optimized using simulated annealing based on the comfort and cost-effectiveness ratio (comparison of alternatives with respect to multiple criteria of different levels of importance for energy usage). The focus is to generate maximum profit from energy sales for monthly profit to be achieved. Prototyped hardware and software are implemented and tested in real-time. The test results show that the 20% reduces energy consumption, and a monthly gain of $89.2 is obtained from energy sales using the proposed method. Therefore, the test results reveal the effectiveness of the proposed architecture and algorithm.

An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring

An advanced smart home energy management system considering identification of ADLs based on non-intrusive load monitoring

Towards simulating the constraint-based nature-inspired smart scheduling in energy intelligent buildings

Appliance scheduling on the user’s defined preferences plays a pivotal role in smart home energy management systems. In this context, energy management controllers are largely used to satisfy the users’ demand preferences within their financial budget constraints. This work proposes a robust technique based on demand-side energy management for efficiently monitoring and controlling the domestic loads. A nature-inspired crescive consumer satisfaction algorithm (NCSA) is proposed for the optimal scheduling pattern based on time and device preferences. The proposed algorithm maximizes user satisfaction at the preset user budget by producing optimum appliance scheduling patterns. It considers household appliances input data such as time of use, power ratings, and the absolute maximum satisfaction for optimal scheduling. The proposed algorithm is evaluated on three budget schemes, and the simulation results reveal that the proposed algorithm achieves a better satisfaction index at a lower cost per unit satisfaction. Results also show that the proposed algorithm has a good convergence rate and is generalizable to any random budget scenario.

Smart home energy management processes support through machine learning algorithms

Smart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts.