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    },
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        "idx": 2,
        "result": "unsupported"
    }
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ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print)

IJCST Vol. 11, Issue 1, Jan - March 2020

Smart Home Energy Management Algorithm
Including Renewable Energy Sources
1
1,2

Halim Halimi, 2Florin Asani

Dept. of IT, Faculty of Natural Sciences and Mathematic, University of Tetova,
Tetova, R.N. Macedonia

Abstract
Increased power demand and integration of renewable energy
sources is impossible with today’s power grid infrastructure. To
overcome these problems, smart grid is the new solution which is
more reliable, flexible and controllable. Home Energy Management
System (HEMS) in the smart home allows the customer to control,
optimize and monitor the energy consumption and the energy
conservation. 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. The home server
monitors and controls the energy consumption and generation
and controls the home energy use to reduce the energy cost.
The remote energy management server aggregates the energy
information from the home servers, compares them and creates
statistical analysis information. We propose the control algorithm
to efficiently manage the renewable energy and storage to minimize
grid power costs at user’s home. The proposed HEMS architecture
is expected to reduce user’s electricity cost significantly.
Keywords
Home Energy Management System, Control Algorithm,
Renewable Energy.
I. Introduction
Smart grid is the integration network of information, communication
and network technologies, which is able to utilize the electrical
energy efficiently, sustainably, reliably and safely. The features
of this power system should be more attractive, secure, reliable
and intelligent comparing to the existing systems. A smart HEMS
is a system between home appliances and energy providers to
optimize energy consumption. Smart homes are characterized
by the presence of smart devices, which give the opportunity to
monitor and to remotely control key equipment within homes.
Renewable Energy Sources (RES), such as wind power, solar
power and fuel cell etc., should be utilized to fulfill energy demand
as well as conventional energy sources based mainly on fossil
fuels. A challenge is by integrating RES into the grid and increasing
power demand causes the redesigning of the conventional power
system architecture and infrastructure.
With the increased concerns on global energy security and
environmental emissions, more and more distributed renewable
energy generations, such as wind turbines, solar panels, and plugin electric vehicles (PEVs), etc., would be grid-integrated into the
active distribution networks. 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 home efficiency of energy conversion and utilization [1].
One of the main research areas in the smart grid is energy
management applications. Energy management applications

22 International Journal of Computer Science And Technology

provide several benefits to both utilities and consumers. Utilities
are able to improve power with higher reliability and stability, and
lower operational costs while consumers can utilize the energy
in cost saving way. Another important valuable benefit with
respect to environmental issues is reducing the greenhouse gas
emission. Several Demand Response (DR) programs are widely
implemented on the commercial and industrial side [2].
Smart HEMS is an essential home system for the successful
demand-side management of smart grids [3]. 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 [4,5,6].
HEM system, as important part of smart grid, provides a number
of benefits such as savings in the electricity bill, reduction demand
in high rate and meeting the demand side requirements.
Several HEM algorithms by which consumers are able to manage
their electricity consumption have been proposed in the literature
[7, 8, 9]. These algorithms are based on different methods such
as: load shifting, optimal scheduling, charges the battery from
renewable sources and from the grid during low rate period etc.
Operating and duration time of home appliances can be shifted
by load shifting and optimal scheduling methods [10]. Many of
HEM system consider only grid supply. In [11], is proposed an
optimal model for HEM in which wind and solar power sources
are considered.
This paper proposes a HEMS framework that includes loads,
batteries, and renewable generation interconnected with the grid
through a home server. The proposed HEM control algorithm
uses load shifting for smart homes. The algorithm schedules the
operation of home appliances, batteries, and renewable generation
as well as the optimal power distribution among loads, batteries,
renewable sources, and power grids.
II. Architecture of Home Energy Management System
Including Renewable Energy
A. System Architecture
Although numerous efforts are taken for energy-efficient home
appliances [12-15], energy management can achieve more energyefficient home. It provides an opportunity for economic benefits
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
[16].
For an efficient smart home energy management, we use a new
architecture, which, in terms of energy management, consists of
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two parts: the part of energy consumption and the part for energy
generation. [17]. Fig. 1 shows the smart HEMS architecture, where
home appliances and lights belong to the energy consuming part,
while wind and solar resources belong to the energy generation
part [17].

IJCST Vol. 11, Issue 1, Jan - March 2020

B. Home Appliances
To implement optimal or coordinated planning strategies of devices,
smart home appliances should be divides into two groups:
1. Non-schedulable home appliances, e.g. fridge, printer,
microwave, TV, hair dryer, lights, computers;
2. Schedulable home appliances, which can be scheduled for an
optimal function or switched on/off at any time, e.g. washing
machine, air conditioner, iron, boiler, Electric Vehicle (EV).
[17]
Devices which can perform their work in an automated form, such
as the air conditioner and boiler, are schedulable. Whereas, nonschedulable devices, such as: lights, computers and TVs, rely on
manual control to complete their operation and are needed only
when users are at home. The users’ comfort is quite sensitive
towards services of non-schedulable devices at a real time.

Fig. 1: Architecture of Smart Home Energy Management System
(HEMS) [17]
In the energy generation part the energy generation from wind
and solar sources, is monitored through renewable energy gateway
(REG). In this structure, the solar system of the energy is consisted
of: solar panels, PLC modems, solar inverters and an REG. The
solar panel operation is controlled and monitored by PCL modem,
which communicates with REG. The PLC modem monitors and
controls the status of each solar panel and the gathered data from all
panels are sent to REG. The generated DC energy by solar panels
is converted to AC energy by the inverter, and it also monitors
the accumulated energy.
The system of wind power consists of wind turbines and inverters.
The accumulated DC energy by wind turbines is converted to AC
energy by the wind inverter, and it also monitors the accumulated
energy. REG gathers data about the solar panel and wind inverter
status and transfers them to the home server via Ethernet. As a
smart device of the HEMS system, the home server aggregates
all information regarding the consumed and generated energy. It
has both, the consumed and generated energy profile at over time.
At any time, it estimates the potential amount of energy that can
be generated by taking into account the atmospheric conditions
obtained from Internet, such as: air temperature, fog, air humidity,
wind speed.

Energy management researchers predict that the amount of
EV will increase in a near future, which would help reduce air
pollutants and greenhouse gas emissions (GHG) [18]. Since EV
can be charged or discharged when connected to the power grid,
an increasing number of practical services can now be realized in
the power grid [19]. Vehicle-to-grid, as a new concept, enables the
transmission of the stored power in the EV battery to the power
grid [20-22]. In a smart HEMS, EV are able to balance the energy
at high rates, which means that EV can supply energy during high
rates, while consumer consume energy during low rates period.
C. Home Server
The home server manages all EMCUs installed on each outlet and
on the light switch via ZigBee, Wi-Fi, UTP/ FTP cable. It also
controls and monitors the performance of all EMCUs through
control elements. The control table manages the home appliances
and lights connected to the EMCU. Through this control table,
the home server identifies home appliances and lighting. The data
related to power consumption of appliances and lighting are stored
in the database, so the aggregated data are accumulated at every
moment. The Energy Consumption Manager (ECM) continuously
analyzes the data collected on a daily, weekly and monthly basis
[17].

The home server manages and controls energy consumption
based on generation estimation. Considering the optimization of
consumed energy, various aspects can be considered from various
analyses and control plans which are based on different algorithms.
But in all these systems, the user is interested to access the home
server through their smart phones for detailed information on
their home energy balance.
If every home individually sends information related to energy via
the home server, then remote energy management server (REMS)
aggregates that information from the home server. REM analyzes
the gathered information and creates new information in numerous
aspects.
w w w. i j c s t. c o m

Fig. 2: Function Blocks of a Home Server [17]
International Journal of Computer Science And Technology 23

IJCST Vol. 11, Issue 1, Jan - March 2020

Data from solar panels, solar inverters and wind inverters are
collected in the REG, and then REG transmits them to the home
server. The transmitted data in itself contains information about
the performance of each solar panel, solar power system and wind
power system. This classified data is gathered and stored in the
database. Weather forecast data is used to estimate the amount
of potential energy generated. The energy generation manager
(EGM) analyzes the renewable energy generation. The solar energy
generation is based on the solar radiation, amount of fog and the
surface of solar panels, whilst wind energy generation relates to
the wind speed. Accordingly, EGM can estimate renewable energy
generation based on the weather conditions. As a result, based on
the estimated energy generation, the home server can modify the
home appliances schedule so that the energy cost is reduced. For
example, during the time of the low renewable energy generation
and in high rate time, the load of several home devices can be
postponed to low rate time. The home server decides this based
on the priority of the operation.
The User Interface (UI) in smart homes provides sufficient
information to home users about energy consumption and
generation. The UI shows the energy consumption and generation
information over time. Users can check and browse energy usage
of each appliance and each light. The home server provides
information to smart devices on request, and they access it
through smart applications. The home server transfers the home
energy information to the REMS, which manages several clienthomes.
D. Energy Management and Communication Unit
(EMCU)
EMCU belongs to the energy consuming devices and consists
of measurement and communication blocks. The measurement
device block measures the consumed energy and the power factor
of home appliances [17]. The power measurement is carried
with UI measuring factor. The measurement block stores the
information about the accumulated energy and calculates the
power and power factor on demand. The measurement block, in
its content, includes also the power control block which enables
switching on or blocking the electric appliance to the connection
to the electric energy. The communication device block enables
the transfer of aggregated information between the EMCU and
the home server. This communication is enabled by ZigBee, Wi-fi
or UTP/STP cable and it transfers data about the voltage, current,
power and power factor.

ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print)

E. Renewable Energy Gateway and PLC Modem
The REG, as a key component in the energy generation part,
communicates to the PLC modems, the solar and the wind inverters
[17]. The PLC modems communicate through TCP/IP protocol,
actually, and IP address is assigned by the router. The measuring
devices of the control block continuously measure voltage and
current in the solar panels. These read data are transferred from the
PLC modem to the REG. Here, the REG has three communication
interfaces: PLC for each solar panel, Ethernet for the home
server, and an RS 485 for the inverter [23]. The PLC modems
and Ethernet communicate through TCP/IP protocol. The solar
and wind inverters are connected through RS 485 interface. The
data aggregator sends a request message to each PLC modem and
inverter, regarding their data status. Thus, the aggregated data are
periodically sent to the home server.

Fig. 4: Function block of PLC modem and Renewable energy
gateway [17]
F. Remote Energy Management Server (REMS)
The home server from each home transfers the aggregated home
energy information to the REMS [17]. The REMS aggregates
all energy information from each home server, from which it
aggregates data on the energy generation, energy consumption by
home appliances and lights. All aggregated information is stored
in the information database, from where, the REMS calculates the
average, maximum, minimum of every home appliance. These
calculated values help to create a standard energy usage pattern,
which serves as a comparing pattern related to energy consumption
by different clients.

Fig. 5: Function block of Remote energy management server
[17]

Fig. 3: Function block of Energy measurement and communication
unit (EMCU) [17]

24 International Journal of Computer Science And Technology

III. An Efficient HEM Control Algorithm
Fig. 6 shows the architecture of energy and communication flow
in a Smart Home Energy Management System (SHEMS). The
most important element of the system is the control center, which
periodically transfers the data collected on energy consumption,
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IJCST Vol. 11, Issue 1, Jan - March 2020

ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print)

and based on the data on previously consumed energy as a pattern,
predicts the expected energy consumption for the next 48 hours.
There are other assisting tools which help in gathering important
information such as: motion sensors, smart phones (for GPS
coordinates), Internet, etc. The control center can also predict the
renewable energy for the next 48 hours, based on the atmospheric
conditions and the weather forecast for the next 2 days. The control
center uses weather forecast to determine how much energy it
should accumulate in the battery, given the prediction on renewable
energy and the expected energy consumption.

			

B. Prediction of Energy Consumption
To predict home energy consumption, we rely on the daily home
consumption of energy, while adapting to seasonal variations as
well. On a normal day, we expect that the total energy consumption
will be similar to the total energy consumption of previous days
with minor differences due to the weather, e.g., a warm day
that does not require air conditioning, or usual daily activities,
such as: use of the clothes dryer, washing machine, etc. More
sophisticated models are the ones which take into account the
changing of standard week activities, weather conditions, or other
information. The goal of this home energy prediction model is
to reduce cost.
EC(t) presents the energy consumed in the t-th day and EPC(t +1)
presents the predicted energy consumed on the (t+1)-th day:
EPC(t+1) = ηEPC(t)+(1-η)EC(t)			

A. Prediction of Renewable Energy
For renewable energy, we use the Sharma et al. [24] prediction
model of generated energy, which enables the calculation of
accumulated energy from solar or wind power, using National
Weather Service (NWS) weather forecast of atmospheric
conditions. In the following work, in order to simplify the work,
we will only focus on solar energy as the predominant renewable
energy, although the mentioned model enables the calculation of
renewable energy from wind as well. Next, we describe the model,
which depending on the amount of clouds in a percentage from 0%
to 100%, enables the prediction of the generated solar energy. The
Algorithm continuously needs weather conditions, sky condition
and other weather parameters in the next 48 hours.
Based on the weather forecast percentage C(t), at any time instance
t, the generated solar energy PS(t) can calculated as:
			

(1)

Where Pmax presents the maximum possible solar generated energy.
Sharma et al. [25] quantify the Equation 1 as more effective
than existing techniques that use the past to predict the future.
According on Equation 1, at any time instance t, the predicted
solar energy within the next 48 hours is as follows:
			

(2)

Where T equals 48 hours, and t = kT. The algorithm run every
evening at the start of the 10 pm low rate period, rather than at
midnight t = 0. For simplicity, we use
to represent
Thus, the Eq (2) can rewrite as follows:
w w w. i j c s t. c o m

(4)

Where η presents a weighting factor of the prediction error over
previous days. Since a pricing model has different electricity rates
at different time intervals during the day, the predicted energy
consumption at the lower rate and higher rate in the (t+1)-th day
can be calculated by Equations (5) and (6),

Fig. 6: Energy and communication flow in HEMS

PS(t) = Pmax · (1−C(t))

(3)

EPCL(t+1) = ηEPCL(t)+(1-η)ECL(t)

			

(5)

EPCH(t+1) = ηEPCH(t)+(1-η)ECH(t)			

(6)

Where ECL(t) and ECH(t) are the actual energy consumption at the
lower rate and higher rate on the t-th day, respectively.
Our goal is to charge the battery when the electricity rate is low,
and discharge the battery to power the home when the rate is high.
Since we analyze the energy conversion efficiency of our system,
i.e. charging the battery during low rate and discharging the battery
to power the home during high rate and adding there and the
inverter’s efficiency [25]. If the energy conversion efficiency is
less than the ratio of the low rate and high rate values, then storing
energy in the battery during low rate periods is cheapest version
than directly using it from the grid during high rate periods.
As an example, using R.N. Macedonia’s model, the ratio of the
lowest rate (2.78 Den/kWh) and the highest rate (5.54 Den/kWh)
is 2.78/ 5.54 = 50.0%. If the energy conversion efficiency is less
than 50.0%, directly using grid energy during the 5.54 Den/kWh
period is more efficient than charging the battery at 2.78 Den/kWh
and discharging the battery during the 5.54 Den/kWh time period.
In this case, 5.54 Den/kWh is not high enough to incentivize
battery-based storage during the high rate period. Since most leadacid batteries have charging efficiencies greater than 85% [26]
and most grid-tie inverters have efficiencies greater than 94%, the
energy conversion efficiency is greater than 85%x 94% = 79.9%,
which is greater than the ratio of the lowest rate (2.78 Den /kWh)
and the highest rate (5.54 Den /kWh). Therefore, both the lowest
rate and the highest rate in R. N Macedonia’s model incentivize
battery-based storage. We use EPCH(k+1) as the expected total
energy consumption in the (k+1)-th day when the electricity rates
are 5.54 Den/kWh.
International Journal of Computer Science And Technology 25

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IJCST Vol. 11, Issue 1, Jan - March 2020

C. Description of the HEM Control Algorithm
We propose an algorithm to minimize the cost of electric energy
from the power grid. The algorithm determines based on: generated
energy in the battery, remaining battery energy (when more home
appliances are switched on), the forecast for energy generation
and consumption for the next 48 hours. Following is presented
the pseudo-code and block scheme of the algorithm. EER(t +1)
presents the expected energy remaining inside the battery that
can be consumed in the (t +1)-th day. EER(t +1) can be calculated
as follows:
EER(t+1) = σER(t)					

(7)

Were ER(t) is the remaining energy in the battery at the beginning
of low rate during the day (t), and σ is the inverter efficiency
coefficient.
ALGORITHM 1: Efficient control
1. if EER(t+1)+ EPS(t+1) >=EECH(t+1)+ EECL(t+1)
then
2. Use the battery to power the house;
3. else if EER(t+1)+ EPS(t+1) > = EECH (t+1)
then
4. while EER(t+1)+ EPS(t+1) -EECH(t+1)>0
do
5. use the battery to power the house;
6. else if EER(t+1)+ EPS(t+1)<EECH(t+1)
then
		

If (low rate)

		
		

7. while EER(t+1)< EPS(t+1)-EECH(t+1)
do

		

8. Charge the battery;

		
		
		
		

9. while EER(t+1)+ EPS(t+1)<EECL(t+1)
do

•

•

•

Postpone Schedulable load

If the amount of battery energy EER(t+1) and the expected
solar energy generation EPS(t+1) is higher than the general
expected consumption, during both, low and high rates, than
the energy from the power grid should not be used in the
house. In this case, the home server commands using battery
energy for home appliances’ operation.
If the amount of battery energy EER(t+1) and the expected
solar energy generation EPS(t+1) is higher or equal to the
general expected consumption only during high rate EECH(t+1),
respectively, EER(t+1)+ EPS(t+1)>=EECH(t+1) than during low
rate, if there is sufficient battery energy, that will be used first,
then the energy from the power grid.
If the amount of battery energy EER(t+1) and the expected
solar energy generation EPS(t+1) is lower than the general
expected consumption during high rate EECH(t+1), then home
server charges the battery during low rate with the sufficient

26 International Journal of Computer Science And Technology

•

charge for the expected general consumption during high
rate, i.e. EER(t+1)+ EPS(t+1)=EECH(t+1).
If during low rate there is not enough generated energy for
appliance operation (battery empty), schedulable equipment
operation is postponed for the time when the battery will
have sufficient power generated, according to the available
postponing operation coefficient of the appliance.

IV. Conclusion and Future Studies
This paper presents a new Home Energy Management (HEM)
algorithm for home to efficiently manage renewable energy,
battery storage, and power grid, implemented in the description
system architecture. Our control algorithm makes the decision
based on the predicted future renewable energy generation and
energy consumption. In this paper, a theoretic detailed description
of the algorithm for home energy management is done, where
RES is connected or not (without RES system) in the system.
The practical implementation of the algorithm will be realized
in the future since it presents a very complex system, especially
the architecture of the control system. The architecture of the
control system, which we have chosen for the implementation of
the HEM algorithm, is also described in this paper. We will have
closer values of cost reduction after the practical implementation
of the algorithm.
In the future, building upon the simulation results of this study, we
will continue to develop our home energy management algorithm
using the real smart home prototype. So the performance of the
algorithm would be evaluated in real environment.
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IJCST Vol. 11, Issue 1, Jan - March 2020

Halim Halimi - Received his B.S.,
M.S., and Ph.D., degrees in Computer
Engineering from Ss. Cyril and
Methodius University of Skopje, in
1999, 2009 and 2014 respectively.
Currently he is a Professor at the
University of Tetova, Tetova, R. N.
Macedonia. His research interests are
smart home, network technologies
and security.

Florin Asani, born 1975 in Gradec Vrapchishte, R. of Nord Macedonia,
Graduated from the Faculty of
Natural Sciences, Department
of Informatics - Tirana, Albania,
2000, Master Graduated from the
Faculty of Natural Sciences, Tirana,
Albania, 2009, Student in Doctoral
School (PhD Candidate) at the
European University of Tirana,
Faculty of Economics, Management
of Information Systems Management - Tirana, Albania, 2012.
Employed as Assistant - Associate at the University of Tetova
- Tetovo, Faculty of Mathematics and Natural Sciences, branch
Informatics, Republic of North Macedonia.
Interest: Intelligent Systems, Multimedia Systems, Robotics.

International Journal of Computer Science And Technology 27
</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.
</statements>

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