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<reference>
https://doi.org/10.1016/j.rser.2016.03.047

This is the Pre-Published Version.

Smart home energy management systems: concept, configurations,
and scheduling strategies
Bin Zhoua*, Wentao Lia, Ka Wing Chanb, Yijia Caoa*, Xi Liua, Yonghong Kuanga, Xiong Wanga
a

College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
b
Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong

Abstract:
With the arrival of smart grid era and the advent of advanced communication and information infrastructures,
bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks
would revolutionize the patterns of electricity usage and energy conservation at the consumption premises.
Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a
profound transition for the energy management pattern from the conventional centralized infrastructure towards
the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources.
Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS)
plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution
systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented.
Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and
reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar,
wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to
reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also
investigated.
Keywords: Renewable energy, Home energy management system, Home energy storage system, Home appliance
scheduling, Smart house.

1 Introduction
In 1998, Electrical Power Research Institute (EPRI) carried out a research of “complex interactive network/
system” to develop a highly reliable and fully automated grid in the United States, which is the prototype of the
U.S. smart grid [1]. Since the EPRI formally proposed the term “Intelli-Grid” in 2002, the concept of smart grid
has been widely accepted to indicate the future development trend of power grids [2, 3]. With the use of the term
“Smart Grid”, European countries founded the “European Smart-Grids Technology Platform” in 2005, and then
launched a research report in 2006 to comprehensively formulate the concepts and framework for the European
smart grid [4]. Later, the U.S. Department of Energy released a report “The Smart Grid” in December 2007, and
it integrated the European ideas and concepts into the U.S. smart grid to support the trend towards a more reliable
and sustainable green energy supply [5, 6]. In recent years, developing a modernized smart city infrastructure has
become a global common priority in most countries because of the tremendous environmental, economic, and
societal benefits that it could offer. In particular, smart electricity usage in demand side plays an important role in
*Corresponding author at: College of Electrical and Information Engineering, Hunan University, 410082 Changsha, China. Tel.: +86 731
8388 9677; fax: +86 731 88664197.
E-mail addresses: yjcao@hnu.edu.cn (Y. Cao), binzhou@hnu.edu.cn (B. Zhou).

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

improving the sustainability and energy conservation for the home end-users, and will also affect the electricity
consumption pattern of human daily behaviors [7, 8]. Recent advancements on information and communication
technologies, such as advanced metering infrastructure (AMI), smart sensor technologies, bidirectional
communication, smart home appliances, home area network (HAN) and home energy storage system (HESS), etc.
have been developed. Therefore, this growing trend provides the technical foundation and infrastructures for the
smart house with home energy management system (HEMS) [9].
Smart HEMS is an essential home system for the successful demand-side management of smart grids [10]. It
monitors and arranges various home appliances in real-time, based on user’s preferences via the human-machine
interface in smart houses, in order to conserve electricity cost and improve energy utilization efficiency [11-13].
With the growing concerns on global energy security and environmental emissions, more and more distributed
renewable generations, such as wind turbines, solar panels, and plug-in electric vehicles (PEVs), etc., would be
gird-integrated into the active distribution networks with gradually increasing penetration [14, 15]. Coupled with
the rapid development in advanced power electronics and alternative energy technologies, building renewable and
stored energy sources installed at the residential premises can be incorporated in smart HEMS to improve the inhome efficiency of energy conversion and utilization [16]. Consequently, this leads to a fundamental transition for
modern energy management systems from traditional centralized infrastructure towards the cyber-physical HEMSs
and autonomous responsive demand with large geographical regions of renewable and stored energy sources
throughout smart power systems [17, 18].
With the two-way information flow between electricity providers and consumers in smart grid, the massive
HEMSs are encouraged to participate in demand response mechanism for energy savings and cooperation. The
demand response is defined as the changes in electricity usage by the end-use customers from their normal
consumption patterns in response to changes in the time-dependent electricity price [19], and it offers incentives
for the demand-side consumers to urge lower electricity usage over periods with higher prices or when power
supply reliability is jeopardized [20]. Furthermore, in response to the real-time electricity price from smart meters,
the home consumers with HEMS can shift their demand consumptions of appliances automatically or manually
into off-peak hours in order to minimize the electricity payment [21]. In a typical smart house, the thermostatically
controlled appliances, including heating, ventilation, and air-conditioning system, electric water heater and
refrigerator, usually account for most of residential energy consumption [22]. The ever-increasing load demand
and energy crisis issue have made the use of smart HEMS more attractive to both the power utilities and customers
[23]. Therefore, with the consent of the customers, the HEMS can play an important role for optimal coordination
and scheduling of various smart appliances and building renewable facilities.
The objective of this paper is to provide a comprehensive review of the development status and research
progress on smart HEMSs with renewable and stored energy sources. First, a brief overview on the architecture
and fundamental functions of HEMS is surveyed. Then, the advanced HEMS configurations and home appliances
in smart houses are analyzed and presented. Third, the utilization techniques for various building renewable energy
resources in smart houses, including solar, wind, biomass and geothermal energy, are reviewed. Moreover, various
home scheduling strategies for optimal operations of smart HEMSs are further investigated. Lastly, the concluding
remarks are drawn.

2 HEMS overview
2.1 Concept of HEMS
Under the smart grid paradigm, the AMI devices enable a reliable two-way communication between power
utilities and home consumers [24]. It provides an opportunity for economic incentives of smart home to manage

the demand-side resources by shifting their electricity usage during peak load periods in response to the changes
in electricity prices. The economic incentives include the saving in electricity bill, the improvement in utilization
efficiency of household appliances and residential energy conservation [25]. Hence, smart HEMS is defined as the
optimal system providing energy management services in order to efficiently monitor and manage electricity
generation, storage, and consumption in smart houses [26, 27]. With the communication and sensing techniques
in HANs, the information collection for energy consumption from all household appliances can be provided, and
the remote real-time monitoring and control for different operational modes of smart home devices can even be
achieved by a personal computer or smart phone [28]. Besides, HEMS can provide not only the optimum utilization
status of home appliances, but also energy storage and management services for distributed energy resources
(DERs) and HESS [29].
2.2 Architecture of HEMS
The overall architecture of a representative smart HEMS is shown in Fig. 1. The HEMS center includes a
centralized smart controller to provide the homeowner with monitoring modules and control functionalities based
on the home communication network [30]. The real-time electricity consumption data from in-home appliances,
including schedulable and non-schedulable appliances, can also be collected by the main panel of smart HEMS to
implement optimal demand dispatch. In addition, the house gateway, such as smart meter, can be utilized as an
interactive communication interface between power utilities and the smart house in real-life deployment. Typically,
the smart meter receives a demand response signal from power utilities as an input to the smart HEMS, and the
optimization of home appliance scheduling can be implemented for the residential demand response [31]. Electric
vehicle (EV) is a special type of schedulable load. It not only consumes energy from power grids to meet the
residents’ transportation requirements, but also provides emergency power for other household loads within the
smart community environment [32]. Currently, the distributed renewable generations in residential areas most
commonly involve solar photovoltaic (PV). The residential on-site energy sources can be fully integrated in the
interactive generation management and operations of HEMS, and allow the smart houses not only rely on the bulk
power from the transmission systems. Due to the inherent intermittence and randomness of solar energy, the energy
storage devices play an important role to improve the power quality and energy efficiency as well as maintain the
energy system reliability [33, 34].
2.3 Functionalities of HEMS
For the purpose of participating in electricity saving and demand response, HEMS should be more flexible
to manage and control smart home appliances, renewable energy resources and HESS [35]. Moreover, the active
control services, including real-time information on the amount of energy consumption and the pricing of energy
in smart homes, can be provided to the consumers on the basis of HEMS. The household consumers can choose
their preferences via the human-machine interface to schedule the service time of various appliances to enhance
their energy utilization efficiency [36]. Fig. 2 shows the typical functionalities of a smart HEMS center with five
main functional modules, including monitoring, logging, control, management and alarm. Further detailed
description on the five HEMS modules are illustrated in Table 1 [36, 37].

3 HEMS infrastructures
The in-home infrastructure of HEMS is composed of smart HEMS center, smart meters, communication and
networking system, HESSs and other smart devices [34]. With these smart infrastructures, HEMS can access,
monitor, control and optimize the performance of various DERs, EVs, household appliances and equipment.
Furthermore, HEMS is capable of supporting full integration of smart appliances and smart home as well as two-

way interaction with users and electric power utilities. Many attractive features of smart grid such as costeffectiveness, flexibility, provision of differentiated services and user friendly advanced smart power technology
with open standard assessment can also be implemented via the HEMS [38].
3.1 Communication and networking system
So far, various HEMSs have been designed based on different communication schemes with hardware
implementation [39-46], such as power line communication [36], ZigBee [39], BACnet [40], Bluetooth [45] and
human-machine interface systems [42, 43]. Extensive investigations have been done on communication and
networking technologies for HANs. Young-Sung Son believed that the combination of a smart meter and power
line communication could provide remote access, facilitate planning, and save the energy consumption of home
appliances [36]. Dae-Man Han suggested a new Smart HEMS based on an IEEE802.15.4 and ZigBee which
divides and assigns various home network tasks to appropriate components [39]. With the support of active sensor
networks which compose of sensor and actuator components, HEMS can integrate diversified physical sensing
information and control various household devices [39]. Also, Kastner Wolfgang introduced the necessity of the
building automation, systems and communication infrastructures based on BACnet [46]. Considering the potential
of the easily embedding Bluetooth technique in communication devices and household appliances, Lilakiatsakun
proposed a method to establish a complete home network using Bluetooth technique [45]. In addition, Fangxing
Li designed a human-machine interface system which can be used in HEMS. It is supposed that HEMS shall have
five main components referred as application processor, communication interface, user interface, sensor interface
and load interface to facilitate user’s operations on this system [42, 43].
Based on the evaluation analysis of various communication technologies, ZigBee is chosen to demonstrate
the proposed HEMS [42]. ZigBee, a wireless communication technology that is developing rapidly in recent years,
has laid solid foundation on networking, security and software-related technical standards. ZigBee makes use of
the world’s most common 2.4 G~2.4835 GHz frequency band. In terms of the low energy consumption and
durability, ZigBee is with the low power consumption, low cost technology and can support a large number of
characteristics of the network nodes, and thus can be used in a wide range of industrial applications [30].
3.2 Smart meters
Smart meters are the advanced energy meters that measure the energy consumption of a consumer and provide
additional information to power utilities using a two-way communication scheme [47, 48]. As a result, customers
are able to make optimal decisions to schedule the electricity usage of in-home appliances as well as actions of
DERs and HESS [49]. As the foundation of data acquisition, data processing and advanced metering equipment
management, smart meters are the latest techniques blended with computer science, modern communications and
measurement techniques. Considering that smart meters are the basis of smart electricity, the functional design of
smart meters should take into account the current facts and future development, such as the access of DER and the
actual capacity of communication channel [47].
Main functions of smart meters include the following [50]: 1) Measuring the multi-period and multi-mode
power rates of active and reactive energy metering usage; 2) Supporting two-way communication, sending data
information and accepting instruction information, such as real-time information query, real-time electricity
standard rates, meter upgrade program settings, etc.; 3) Enabling the response in terms of the requirements to
achieve smart load shedding and cooperating with smart meter and smart interactive terminals during the islanding
transition when a failure happens on the main power grid; 4) Collecting data with smart gas meters, water meters
and other versatile value-added services.
3.3 Smart HEMS center

Smart HEMS center, analogue to the brain of entire smart electricity home, is the core of HEMS and
implements the smart energy management. Smart HEMS center is located in user’s houses as a home appliance.
Main functions of smart HEMS center are as follows [51]: 1) Receiving a large amount of data sent by smart
meters, main control panel, and real-time display. The control commands issued by the consumer are sent to all
household equipment. Consequently, the automated demand response can be achieved; 2) Providing a friendly
human-machine interface and supporting user’s real-time browsing, online monitoring, task setting and other
functions to arrange the usage of electricity; 3) With high scalability, the smart HEMS center can set water,
electricity, gas, and other indoor controls; 4) Integrating DERs, energy storage devices and electricity regulator of
EVs as well as analyzing and forecasting distributed generations to achieve the optimal control of DERs.
3.4 Home appliances
Various household appliances and energy storage devices can be thoroughly analyzed and modelled based on
the device characteristics and preference usages. In order to implement the optimal coordinated appliance
scheduling strategies, the smart home appliances can be divided into two categories: 1) Non-schedulable home
appliances, e.g. refrigerator, printer, microwave, television, hair dryer; 2) Schedulable home appliances, which can
be scheduled for optimal operation or switched on/off at any time, e.g. washing machine, air conditioner, iron,
water heater, EVs. The appliances which can complete a task without any manual control, such as air conditioner
and water heater, are schedulable [51]. While non-schedulable appliances, such as lights, computers and televisions,
rely on manual control to complete a task and are needed only when the users are home. Since the comfort level
of users is quite sensitive to the timely services of non-schedulable appliances, their usage would usually not be
delayed.
Schedulable appliances can be further classified into ‘interruptible’ and ‘non-interruptible’ in terms of the
continuity of operation time [52]. Generally, the interruptible appliances are usually more schedulable than noninterruptible ones. The non-interruptible appliances are constrained by fixed operation period called ‘hold-time’
[52]. As a unique load, the EV becomes more important and extensive as the part of HEMS. Experts expect that
the amount of EVs will be increased in the following years, which helps to reduce air pollutants and greenhouse
gas emissions [53]. Since EVs can be charged or discharged when connected to the grid, a growing number of
practical services can now be realized in power grid [54]. Vehicle-to-grid, as a new concept, indicates that the
electric energy stored in the EV battery can also be transmitted to the power grid [55-57]. In the smart HEMS, EVs
are capable of balancing the peak power, which means that EVs can supply power during peak periods while users
consume power during off-peak periods.

4 Renewable energy resources in smart houses
4.1 Current status of renewable energy sources in smart houses
Since 1990s, the utilization of renewable energy sources has increased with an average annual rate of 2.0%
[58]. Additionally, renewable energy has been consumed in various fields, including the industrial, residential,
commercial, and public sectors. As shown in Fig. 3, only 31.1% of renewable energy is used for electricity and
heat production worldwide, while 50.4% is used for residential, commercial and public purposes in 2012 [58]. It
also indicates that the research on the use of renewable energy in HEMS is of great significance and developing
prospects. With the rapid development of sustainable energy technologies and increasing demand for low-emission
generations, the utilization of renewable energy shows promising prospects for smart houses. From the technical
and economic aspects, it is quite feasible to substitute fossil fuels with renewable energy for home electricity
supply [59]. Meanwhile, with further development of smart grid technologies including communication and

monitoring, control, and self-healing, the smart home energy utilization has been improved to accommodate
multiplying renewable resources [60].
Due to its rapid development of renewable energy resources in the past 15 years, Germany is a forerunner in
renewable electricity [61]. In 1998, Germany began to implement “100,000 Roofs Plan”, aiming at obtaining 300
megawatts solar power from the roofs of citizens. Presently, there are about 0.9% of German families using solar
power system. Residents sell rooftop solar electricity at a high price to power grid during the day, however, at
night, they buy electricity for use at a fair price. Therefore, residents become energy producers and consumers [62].
Thanks to its easy installation and low cost, the solar PV is widely used in smart houses. Cyprus, known as a leader
in solar water heater in the world, is a country where 92% families and 53% hotels are using solar water heater
systems, equipping with about 937,363m² of solar collectors, nearly 1 m² per person [63]. Moreover, the solar PV
cell productivity in China in 2008 was about 4 GW, 3 GW PV of which was module capacities. The cumulative
capacity of installed PV power was 150 MW. At the same time, the solar water heaters have covered over 125
million m² in buildings, reaching about 60% of the worldwide total [64].
Wind power is characterized by many merits such as clean, renewable, widely distributed and land-saving
[65]. Therefore, wind power is more widely used than other resources to solve severe environmental problems.
For example, in 2013, one third of Denmark’s electricity is produced by wind and over 83 countries in the world
are using wind power to produce electricity [66-68]. Estimates show that the annual electricity generated by wind
power system will amount to 1.7~5.0 TW h by 2020 if new buildings and existing buildings are all equipped with
wind power systems [69-71]. Obviously, the utilization of wind power in smart houses is of great potential.
As an important means of access to energy, the application of biomass combustion power generation
technology in smart houses is foreseeable. Since the 1990s, Denmark, Austria and other European countries have
started the development and research of biomass power generation technologies. After years of efforts, boilers
have been developed for power generation with wood chips, straw, chaff, etc. At present, biomass power generation
has been used in households throughout Denmark [64]. Straw power generation and other renewable energy
sources have accounted for more than 24% of the national energy consumption. The installed biomass power
generation capacity in HEMS of United States has reached 10.5 million kW, while the biomass power generation
techniques get faster development and higher utilization rate in Japanese cities. Likewise, China’s utilization of
biomass has been developed substantially. Achievements have been made in biogas, biomass power generation
and liquid bio-fuel, while the majority usage belongs to electricity and heat generation. The installed capacity of
biomass power generation increased to 4 GW in 2010, which was about 25% of the total. 1600 large-scale digesters
and over 30 million home biogas digesters were built for smart home, and the annual biogas output was about 14
bcm and 1.65 Mt for bio-fuel [64]. Furthermore, great importance has been attached to the combination of biomass
power and smart home energy dispatch. The immature technologies, the absence of comprehensive product
standards, and inadequate supply chain are major barriers to the further development of biomass energy in smart
houses.
Geothermal energy, defined as the heat from the underground, is an ideal renewable energy resource that is
not intermitted and

distributed usually around volcanic areas. There are two forms of geothermal energy

utilization: geothermal heating and geothermal generations, and the latter has experienced a rapid development
over recent years in the world [72]. So far, the chief geothermal energy suppliers for households are the United
States, Iceland and Philippines. All around the world, Icelandic geothermal power is widely used to generate
electricity due to its reliable base load and low cost. Thus, 87% of the smart houses in Iceland are heated by
geothermal energy [73].
4.2 Utilization of renewable energy resources in HEMS
The world environmental condition, increasing need for energy demand nowadays, and the development of

alternative energy technologies have brought forth a large amount of opportunities to develop various sustainable
resources. Various building renewable energy resources, including solar, wind, biomass, geothermal energy and
HESSs, have been utilized in smart houses.
As of today, solar energy is the richest inexhaustible and clean energy among all kinds of renewable energy
resources [74]. Solar energy can be utilized in various aspects including solar water heater (SWH), solar PV, solar
drying and solar cooling, etc. Due to easy installation and low cost, the SWH is widely used in households. Hot
water heated by the solar energy can be used for showering, cooking and washing. In order to improve the
conversion efficiency of SWH and reduce electricity consumption, legislations have been enacted to install SWH
on each new building. Besides, more electricity bills are charged for the household who uses electricity to heat
water [75]. Solar energy utilization can be classified into two types: solar thermal and solar PV. PV technique,
known as the best means to convert solar energy, can directly convert the sunlight into electricity without any help
from heat engines [76]. The solar PV is prevailing among residences where the annual sunshine is abundant [77].
Owing to its easy installation and maintenance, solar PV is quite suitable to incorporate into smart home to provide
locals with energy and be utilized in various ways with less conversion failures. In addition, HEMS is usually
equipped with an HESS which could store the energy for future or emergency use. The HESS also works as a
buffer to make the HEMS reliable and improve network control [78, 79]. The solar PV module is the most
important part of a solar home system (SHS) and its supporting components should be properly adjusted according
to direction and tilt of the module. Apart from the low price, the roof mounting for solar PV module is more suitable
than ground or pole mounting and requires less wiring. The supporting components should be firmly attached to
the roof beams instead of roof tiles [80]. In a stand-alone PV system, it is the irreplaceable charge controller to
protect the battery from overcharge and over discharge. The charge controller is necessary for systems with
unpredictable loads, user intervention, optimized or undersized battery storage and other characteristics that would
allow excessive battery overcharging or over discharging, as the low voltage load will shorten the battery lifetime
and diminish the load availability [81]. DC/AC inverters are used in SHS with a power larger than 100Wp to
provide customers with AC electricity. Only a few types of inverters are suitable for SHS, which are of square
wave, modified square wave, and pure sine wave [82].
Wind energy is an important alternative energy, and its utilization in smart houses is always a hot topic over
the years. Typically, the fan can generate electricity at the wind speed of 2.7m/s, achieve rated power at 25m/s,
and ensure continuous electricity generation at 40m/s. The device required by wind power generation is called
wind turbine generator, which generally includes fan, generator, steering gear, tower, restricting and safety
mechanism, and energy storage device [65]. In smart houses, the building hinders the linear motion of the air so
that the air deflects to the top and sides of the building and produces pressure difference around the fan, which
makes the generator rotate and generate electricity. Usually, the electrical energy generated is generally used for
home lighting equipment, communication equipment and electrical tools [83]. Due to the volatility and
intermittency of wind generations, the specific plans on the adjustable reserve capacity are of intense importance.
Also, an accurate forecast on wind speed is indispensable to reduce the reserve capacity with the improved
reliability [84]. HEMS can provide a well-designed storage platform to schedule the electricity generated by wind
turbines through the charging/discharging strategies of HESS.
Primarily used for home cooking, heating and lighting, the biomass energy is widely used in smart houses.
Numerous investigations have been studied on the biomass energy utilization, including the biomass energy in
high-rise buildings [85], the biomass exploited for building heating [86], the influence of biomass boilers on energy
rating [87, 88], and the biomass fueled tri-generation system for selected buildings [89]. At present, the biomass
power generation used in smart houses includes biomass combustion generation, biomass gasification generation,
and biogas power generation. Biomass combustion generation is to combust biomass with excess air in the boiler,
then the hot smoke generated exchanges heat with the heat exchanger of the boiler, and the high temperature and

high pressure steam generated expands in gas turbine and hence generates electric power [86]. Biomass
gasification generation technique means the thermochemical conversion of biomass into gas fuel, which is put into
the combustion chamber of boiler, diesel generator, internal combustion engine generator and gas engine after
purification to generate electricity. Lastly, biogas combustion generation is a biogas utilization technique that
appears with the development of biogas technique. Biogas is utilized in the engine and equipped with integrated
power generating units to product electricity and heat, constituting an important way for efficient utilization of
biogas [89].
Geothermal energy is characterized by low cost and cleanness, and the most traditional and popular usage of
the energy is to generate heat directly [90]. Over the last decades, plenty of geothermal devices have been installed
in smart houses. Compared to the deep geothermal energy utilization, the shallow systems do not need the
extraordinary geological settings and high geothermal gradients with clean and eco-friendly traits [91-93]. So far,
the main utilization of shallow geothermal energy is air conditioning used in residential households, and the heat
pump techniques are applied to pump up and utilize the shallow underground low-temperature heat sources.
Ground source heat pump is an efficient energy-saving air conditioning technology to utilize shallow geothermal
resources for heat supply and cooling [72]. The exploration and development for geothermal resources is an
emerging industry with high investment and high risk due to the geographical distribution limitations of geothermal
energy [92].
4.3 Techniques to renewable energy in smart HEMS
The alternative energy techniques in smart houses generally refer to the renewable resources developed and
utilized using smart grid technologies. In this section, home energy storage system, hybrid renewable energy
systems, power electronics as well as control and communication technologies are surveyed.
HESS techniques have an important impact on the utilization of renewable energy in smart houses. Recent
commercial advancements in large energy storage and power electronic technologies offer new opportunities in
intermittent generation stabilization, optimum management, power quality improvement, and peak load shaving.
Currently, lead acid batteries, flow batteries, ultracapacitors and chemical energy storages have been widely used
in HESSs [94, 95]. The electricity generated by solar and wind energy is always fluctuant and volatile, and the
balance between energy supply and demand at any time is required in household energy systems. Consequently,
the coordinated charging/discharging schemes of HESS can effectively balance the variability and volatility of
renewable energy generations and maintain a stable and reliable power supply. Moreover, in smart houses, the roof
mounting solar PV, wind energy and other DERs are main power sources during the peak-load periods, and the
energy storage devices and EVs in HESS are of the essence for interactions between households and power utilities
[59]. On the other hand, during the electricity outage periods, the HESS can be employed with various renewable
generations to form an independent generating installation to provide electricity supply for the critical load
consumers so as to enhance power grid resilience [29].
Due to its inherent seasonality, variability and periodicity, a single renewable energy source, such as solar,
wind, and geothermal, is inadequate to provide a continuous and economic power supply for HEMS [96]. Thus,
the hybrid energy system with multiplying renewable generations can be formed and utilized to alleviate the
intermittent and unstable effects of electricity supply [59]. For most of HEMSs, the sunlight is sufficient with
abundant power generations of PV panels in summer, and thus more energy from PV can be used and stored for
electricity supply. On the other hand, sunlight will be weakened in winter and wind power is the main contributor
to support more electricity supply. In addition, there are various configurations for different hybrid renewable
energy systems in smart houses, such as wind/PV, PV/biomass, wind/hydropower, wind/PV/biomass, and so on
[97].
Since most of renewable and stored energy resources are transformed into electrical energy in smart houses,

the energy conversion technologies are critical for the utilization efficiency of various renewable energies. The
major impact of power electronic conversion technologies on renewable energy development and utilization is to
improve power quality, energy efficiency-peak shaving, and control strategies [19]. Power electronic techniques
have been increasingly adopted in household energy generation system for grid-integrated solar and wind energy
sources [82]. With the extensive utilization of renewable generations and energy storage devices in residential
buildings, it is necessary to design the optimal sizing and configurations for power electronic converters [11]. Also,
with the highly efficient power electronics in alterative generations, energy management and conversions, userend interfaces, together with the advanced control solutions, can pave the way for large-scale applications of
renewable energies in smart houses [17]. In smart houses, PV systems generate DC power which needs to be
converted to single- or three-phase AC, while the wind and microturbine systems generate variable frequency AC
output which needs to be converted into the rated frequency AC for appliance utilization. For battery energy storage
systems, a bidirectional DC-DC converter followed by a DC-AC inverter is the most general choice [43]. An
increasing penetration level of renewable energy systems results in more stringent household demands, and the
tasks of power electronics based HEMS are as varied as they are demanding by local end-customers [42]. The
specific demands can be summarized as: 1) reliable/secure power supply, 2) high efficiency, low cost, small volume,
and effective protection, 3) control of active and reactive power generations, 4) dynamic ride-through operation,
and 5) system monitoring and communication in HEMS [11].
Communication and information technologies are required in smart HEMS to implement optimal appliance
scheduling and energy management strategies. Within a smart home, the HEMS center with metering devices can
collect and deliver information and control signals to optimize the electricity consumption and production schedule
[30]. So far, different communication standards have been envisioned within HAN, such as HomePlug (IEEE
P1901), Ethernet (IEEE 802.3), X10 (X10 standard), Insteon (X10 standard), ITU G.hn (G.hn), Z-Wave (Zensys,
IEEE 802.15.4) WiFi (IEEE 802.11, IEEE 802.15.4), ONE-NET (Open-source) 6LowPAN (IEEE 802.15.4),
ZigBee (IEEE 802.15.4), and EnOcean (EnOcean standard) [98]. Therefore, for effective utilization of renewable
energies in HEMS, the devices require the ability to broadcast vital information about their state and energy
requirements, as well as act based on signals received from the HEMS [39].

5 Energy scheduling strategies in smart houses
5.1 Appliance scheduling strategies for HEMSs
In the deregulated power markets, the implementation of demand response can loosen the control of retail
electricity prices and improves the elasticity of demand [99, 100]. In recent years, various decision-support tools
have been reported to optimize and implement the home appliance scheduling with electrical energy services for
residential consumers in smart houses [101-109]. The application of PEVs as dynamic energy storages with their
travel patterns to coordinate the optimal home energy scheduling in a residential community has been presented
in [102-104]. Automatic energy consumption scheduling strategies with price predictors was proposed in [105,
106] to minimize electricity payment in a real-time pricing tariff environment. In [51], an energy management
controller was developed on the basis of demand response information from the home gateway, and all appliances
in smart houses will operate automatically in the most cost-effective way. Considering various uncertainties on
appliance operational time, intermittent renewable generations and variations of electricity prices, the stochastic
efficient scheduling schemes for optimized HEMSs have been addressed in [107, 108]. Moreover, the distributed
control algorithms for household demand response have been presented in [52, 109] based on the bidirectional
communication network architecture to schedule the in-building appliances and renewable energy sources.
In this context, many countries and regions conducted extensive investigations and practices for the demand

response, especially the United States who helped organizations carry out the demand response and achieved
satisfactory results. In August 2005, US President George W. Bush signed the Energy Policy Act. The bill clearly
defines that the implementation of demand response and enforces it with great support. In addition, a study of
demand response was submitted by the US Department of Energy to the Congress in February 2006, elaborating
on the benefits of the implementation of demand response and recommendations [110]. Subsequently, the US
Federal Energy Regulatory Commission also submitted the annual report of demand response to the Congress in
August 2006 and September 2007 which analyzed the background and status of implementation of demand
response in residential households, the influence of demand response on power grids as well as the applications of
AMI in the demand response and smart houses [111, 112].
5.2 Price-based and incentive-based demand response strategies
According to the research reports from US Department of Energy, in accordance with the user in different
ways to respond, the demand response division of power market includes the following two types: price-based
demand response and incentive-based demand response [109]. Table 2 shows a comparison of the two demand
response schemes [110]. The price-based demand response refers to the retail prices when users are in face of
changeable electricity demand, including time-of-use pricing (TOU), real-time pricing (RTP) and critical peak
pricing (CPP) and so on. User decision-making process through the internal economy would change the periods
of low electricity price and reduce the electricity consumption in high-price periods to conserve electricity costs.
On the other hand, the incentive-based demand response stands for the demand response implementation
mechanism through the formulation of a deterministic or time-varying policy, including direct load control (DLC),
interruptible load (IL), emergency demand response (EDR), demand side bidding (DSB), and capacity/ancillary
service program (CASP) and so on [110]. The function of incentive-based demand response presents its capability
in motivating users in the impact of system reliability or timely response by higher electricity prices and demand
reduction [99]. These two types of demand response strategies are inherently relevant and complementary to each
other, and Fig. 4 illustrates their dispatch and commitment under the conditions of different energy utilization
efficiency and time scales in HEMS [110]. As can be seen in Fig. 4, in real-time appliance scheduling strategies,
demand response not only can be flexibly deployed on different time scales, but also participates in the
coordination of various appliance management and renewable generation scheduling [113, 114].
Modern smart grid infrastructures with two-way communication enable power utilities to provide end-users
with a time-dependent electricity price for interactive demand response. Based on communication and sensing
techniques in HANs, a smart HEMS center can therefore schedule an optimal real-time and price-responsive
electricity usage scheme for home energy storages and residential appliance loads, including schedulable and nonschedulable appliances [100]. The principal objective of optimal demand response scheme of HEMS is to manage
the power consumption of home appliances during peak and off-peak periods in order to reduce the electricity cost
of consumers and improve the utilization of energy from power generation utilities. Furthermore, HEMS should
also schedule and coordinate the power generated from weather-dependent renewable energies, energy storage
devices and user comfort levels to make more reliable and efficient use of electricity in an optimum way. Thus,
there arises an urgent requirement for an optimal appliance scheduling scheme of HEMS with renewable and
storage energy sources in smart grid environment [101, 102]. The progressive development of smart grid
infrastructures and advanced energy storage devices has brought new opportunities and challenges in demand
response. The smart HEMS should cope with increased uncertainties of renewable energy sources in a large scale,
HESS, and consumer comfort preference on home appliances [113]. Besides, a smart HEMS shall be able to
respond to renewable generation fluctuations, electricity price, and other human behavior influences in real-time
or near real-time to achieve a comfortable lifestyle with financial incentives [114]. The system shall also be flexible
enough to accommodate and manage various home appliances, renewable energy resources and HESSs for energy

saving and demand response.
5.3 Modelling and control schemes for household appliances
The emergence of smart grids and the increasing concern for electricity saving have presented opportunities
for smart HEMS in demand response markets. As an important demand response tool, HEMS shift and curtail
household appliance usages to improve the energy efficiency and production profile of a household on behalf of a
consumer. HEMS usually formulate optimal consumption and production schedules with the consideration of
multiple objectives such as energy costs, environmental concerns, load profiles, and consumer comfort [115]. In
general, the main objective of control schemes for household appliances in HEMS is the minimization of energy
consumption [115]. Besides, various artificial intelligence techniques have been applied to the control of both
conventional and bioclimatic buildings. Intelligent controllers, optimized by the use of evolutionary algorithms
were developed for the control of the subsystems of an intelligent building and in-home appliances [116]. The
synergy of the neural networks, with fuzzy logic, and different evolutionary algorithms resulted in the so-called
computational intelligence, which now has started to be applied in smart houses [117]. Moreover, the detailed
modelling considerations for HEMS, including modelling demand response of devices, modelling of well-being,
modelling multi-objectivity, modelling for uncertainty and communications infrastructure requirements inferred
based on modelling, are surveyed in [118].
Since each appliance has unique characteristics, difficulties may arise for the HEMS to intuitively develop a
model that represents each device. Beyond the infrastructure and communication challenges associated with the
dynamic inclusion of new devices, such as recognizing one’s plug-in hybrid. EVs returning home, the modelling
and control of a multitude of appliances can be an evident barrier to the deployment of HEMS optimization [39].
Well-being analysis, relating to the lifestyle of residential consumers, is considered as an important objective to
maintain in managing energy consumption [97]. The loss in the quality of service caused by energy delivery can
bring inconvenience to the consumer. It is unsuitable to simply infer the inconvenience incurred to the consumer
as a result of a shifted load because inconvenience is not time-invariant while consumers are heterogeneous. The
work in [69] elaborates on multi-objective optimization theory and applications, and further describes the types of
multi-objective optimization formulation. Incorporating uncertainty into the scheduling process has the potential
to improve scheduling efficiency. Model predictive control is the most common method for addressing forecast
errors, and is an open-loop online control system that approximates the desired solution by reducing the impact of
the undesired dynamic properties of the system. According to [112], model predictive control is a methodology
rather than a single technique. The HEMS modelling requirements are dependent on the market and
communication infrastructure. In case where information from the grid is minimal or unnecessary, the HEMS only
needs to communicate with the devices in the house. When frequent or dynamic signals are sent from the grid, the
HEMS passively receive the information from the grid, either through the internet or through broad wireless signals
[118]. Finally, when active bi-directional communication is required between the HEMS and the utility, the HEMS
must be designed to send information frequently, securely, and with an effective communication protocol.
5.4 Scenarios analysis for HEMS scheduling strategies
A smart HEMS is a residential demand response tool that shifts and curtails demand to improve the energy
efficiency and reduce electricity cost based on the real-time electricity price and consumer comfort [117]. In this
section, the HEMS scheduling strategies are analyzed in different scenarios. The HEMS finds the optimal operation
schedule by using a scheduling algorithm, and dispatches signals appropriately. Study on the efficiency of HEMS
based on Marc Beaudin and Hamidreza Zareipour indicates that a HEMS could reduce operational cost of
electricity by 23.1% (mean of 25 references), or reduce residential peak demand by 29.6% (mean of 18 references)
[118]. In addition, according to [98], other advantages of using HEMS include minimization of energy wastage,

reduction of household occupant intervention, eco-friendliness, and improvement of resident well-being. In
practical applications, not all users can simultaneously obtain the utilization of power load control, HESSs, DER
and the opportunity of selling electricity to the power grid. However, a mass of research indicate that energy
consumption fees can be decreased if users possess HESSs, DER, and the chance of selling electricity to the grid.
On the basis of merely considering the electricity load in optimal operation computing method, [119] successively
added accumulation system, PV power generation system and user’s ability to sell electricity to the grid to optimize
system dispatching by using three different calculation methods under every circumstance. The corresponding
electricity usage cost of users is shown in Table 3 [119]. The technology capabilities are incrementally increased,
starting with load scheduling only, and then adding successively the ESS, the ability to sell electricity, and the
ability to generate electricity (solar panels, and finally genset). Simulations were run on an Intel Xeon 4-core
processor at 2.53 GHz with 6 GB of memory. The average running time of the algorithm in [119] for the scenarios
presented in Tables 3 was 231.8 seconds, with a maximum at 414.9 seconds. From a practical implementation
standpoint, the algorithm could be run either locally, assuming the presence of a good solver, or by third parties
providing computational capabilities to the residential consumer. Table 3 shows that, for a given technology
portfolio, the algorithm in [119] always outperform the two base cases. In the basic scenario with the user limited
to scheduling controllable loads, the economic gain varies from 9.8% (against B-DA case) to 28.3% (against BRT case). In presence of the ESS, the algorithm in [119] improves the objective function by 21.2% to 37.7%. If
the consumer also has the ability to sell power, the proposed method outperforms the base case by 23.3% to 53.8%.
Finally, if the solar panel is also connected to the home grid, the algorithm in [119] outperforms the base case by
34.9% to 86.6%. Note that the B-RT and B-DA scenarios limited to load scheduling capabilities always lead to
higher bills than the corresponding flat price scenario. This illustrates suboptimal energy utilization when
residential customers are to deal with dynamic pricing environments not equipped with the proper decision tools.
It can be concluded that the modeling framework proposed in [119] will lead to significant economic savings for
both the consumer and provider.

6 Conclusions
In the smart grid environment, the smart house with its HEMS plays an important role in the intelligent use
of electricity and demand response. The smart HEMS with wireless networks, smart home appliances, digital
citizen services, and smart sensor technologies could elevate standards of living and commerce with social and
environmental capitals. In recent years, the popularity of HEMS has been increased significantly due to high
accessibility, convenience and affordability via smart phone and tablet connectivity. Meanwhile, the development
of modernized smart grid infrastructures with a variety of two-way communication, metering and monitoring
devices lays a solid foundation for smart HEMS application. In the near future, the extensive use of HEMS will
thoroughly change the way of electricity usage and renewable energy utilization in the residential houses. In this
paper, the introduction and description for the overview of HEMS architecture have been illustrated, as well as a
detailed investigation on the functions of renewable energy, HESSs, home appliances and demand response in
HEMS. On the other hand, the utilization of renewable energy in buildings is becoming increasingly widespread.
Findings show that solar energy is a main contributor to home energy consumption, while the wind, biomass, and
geothermal energy often contribute relatively less and limited due to urban geography and climate factors. The
utilization of building renewable energy also demonstrates that remarkable energy savings could be achieved from
transmission energy losses and traditional primary energy. Furthermore, the home scheduling strategies for smart
appliances, renewable energy and HESS have been investigated and analyzed to reduce the residential electricity
cost and improve energy utilization from electric power utilities. Consequently, developing the smart HEMS has
become a common global priority to support the trend towards a more sustainable and reliable green energy supply

for smart grid.

Acknowledgements
The authors gratefully acknowledge the support of the National Natural Science Foundation of China (Key
Program: 51137003), the National Natural Science Foundation of China (51507056), the science and technology
project of State Grid Corporation of China (Research and application of key technologies in smart grid park energy
management and optimization for smart city), and also would like to express our sincere thanks to the organizations
and individuals whose literatures have been cited in this paper.

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Table 1
Functional modules of HEMS
HEMS modules

Service description
Monitoring offers easy access to real-time information on energy consumption and allows users to focus

Monitoring

on the electricity saving. It can also provide display services for the operational modes and energy status
of each home appliance.
Logging is to collate and save the data information on the amount of electricity usage from appliances,

Logging

generations from DERs and energy storage state. This service also contains demand response analysis for
real-time prices from grid utility.
There are two types of control, namely, direct control and remote control. Direct control is implemented

Control

on both the equipment and control system; whereas, remote control means customers can online access to
monitor and control the usage patterns of in-home devices via handheld personal computer or smart
phone from outside.
Management is the most important function of HEMS to enhance the optimization and efficiency of

Management

electrical power usage in smart house. It covers a range of services including renewable energy system
management service, energy storage management service, home appliance management service, and
Plug-in EV and battery management service.

Alarm

Alarm will be generated and sent to the smart HEMS center with information on the fault locations, for
example, if there is any abnormality detected.

Table 2
A comparison of two demand response schemes
Schemes

Options

Functional descriptions
A pricing strategy where the electricity prices vary with time, usually

Time-of-use pricing

designed over a 24 hour day.
Price-based demand

A pricing strategy in which the price for electricity typically fluctuates

Real-time pricing

hourly, reflecting changes of wholesale price of electricity.

response

A pricing strategy where electricity prices are determined beforehand

Critical peak pricing

at peak hours of critical day.
In order to solve the reliability problem of system or regional
Direct load control

emergency, the program operator remotely shuts down or cycles a
customer’s electrical equipment on short notice.
Reducing or interrupting the participation of load in emergency

Interruptible load

situations of the system.
Customers offer bids for curtailment based on wholesale electricity

Demand side bidding

Incentive-based

market prices or an equivalent.

demand response
Emergency demand response

Processing the system reliability accident caused by network operating
risk, power shortages, blackouts, and so on.
Dealing with the generator failure, transmission line fault and other

Capacity/ancillary service

system accidents caused by the tight capacity. It can also participate in

program

voltage control, frequency regulation and demand side resources
reserve.

Table 3
Comparative analysis on electricity cost under different scenarios
Base case with

Base case with

real-time pricing (B-RT

day-ahead prices (B-DA

case) ($)

case) ($)

Appliance scheduling

14.35

14.17

12.78

Adding energy storage system

13.50

13.02

10.26

Adding electricity selling

13.48

11.52

6.23

Adding solar PV panels

4.39

6.43

0.59

Adding DER set

2.53

6.22

0.42

Scenarios

The robust approach
proposed in [119] ($)

Figure 1
Overall architecture of a representative HEMS

Figure 2 Functionalities of a representative smart HEMS

Figure 3 World sectoral consumption of renewables in 2012

Figure 4 Demand response scheduling strategies in smart HEMS
</reference>

<statements>
1. Academic and industrial research consistently identifies Home Energy Management Systems as a core architectural component of future smart homes, responsible for monitoring and optimizing generation, storage, and consumption. HEMS coordinate smart meters, smart plugs, distributed energy resources (DERs) such as rooftop PV and battery energy storage systems (BESS), and controllable loads like appliances and HVAC under demand‑side management and time‑of‑use tariffs.
2. Integrated energy dashboards and tariffs-aware smart plugs enabling granular, circuit-level control linked to HEMS and utility APIs.
3. Products that unify HEMS, DERs (PV, BESS), and EV charging into a single orchestrated platform—exposed through mobile apps, voice assistants, and utility APIs—are expected to grow rapidly. These platforms will feature dynamic load balancing, tariff‑aware scheduling, V2H/V2G capabilities, and resilience modes (e.g., islanding during outages), positioning the smart home as an active grid participant rather than a passive consumer.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.