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316 IEEE Transactions on Consumer Electronics, Vol. 59, No. 2, May 2013

Contributed Paper

Manuscript received 03/30/13

Current version published 06/12/13

Electronic version published 06/12/13. 0098 3063/13/$20.00 2013 IEEE

Smart Heating and Air Conditioning Scheduling

Method Incorporating Customer Convenience

for Home Energy Management System

Hyung-Chul Jo, Sangwon Kim, and Sung-Kwan Joo,Member, IEEE

Abstract AHome Energy Management System (HEMS)

is expected to be vital for saving energy costs considering the

time-varying price of electric power in a smart home

environment. Studies on various energy resources such as

energy storage systems and fuel cells in a smart home

environment are required for HEMS development. In the area

of the HEMSs, however, there exists very limited research on

heating and air conditioning scheduling incorporating

customer convenience. This paper presents a smart heating

and air conditioning scheduling method for HEMS thatconsiders customer convenience as well as characteristics of

thermal appliances in a smart home environment. The

prototype software based on the proposed method for HEMS

is also implemented1.

Index Terms Home energy management system, smart

heating and air conditioning scheduling, customer convenience,

energy resource scheduling

I. INTRODUCTIONTime-varying retail pricing schemes are being designed by

utility companies for reducing the increasing energy demand.

A Home Energy Management System (HEMS) [1]-[5] canplay an important role in saving the energy costs considering

the time-varying price of electric power in a smart home.

Wireless communication networks that control and monitor

appliances in a smart home have been studied. HEMSs that

incorporate communication technologies for reducing energy

costs have been developed. In [6] and [7], a model and an

algorithm for a HEMS for coordinating the operating schedule

of a few devices in a building and a micro-grid were

proposed; however, electric vehicle (EV), fuel cell (FC), and

energy storage system (ESS) lifetime were not considered.

In addition, very limited research has been conducted in the

area of scheduling algorithms that control thermal appliances

1This work was supported by the Human Resources Development of the

Korean Institute of Energy Technology Evaluation and Planning (KETEP)

grant funded by the Korea government Ministry of Knowledge Economy

(No.20114010203010)

H. -C. Jo is with the School of Electrical Engineering, Korea University,

Seoul, 136-701, Republic of Korea (e-mail: [email protected]).

S. Kim is with Future IT R&D Lab., LG electronics, Seoul, Republic of

Korea (e-mail: [email protected]).

S. -K. Joo is with the School of Electrical Engineering, Korea University,

Seoul, 136-701, Republic of Korea (e-mail: [email protected])

in a smart home environment, such as heating, ventilating, and

air-conditioning (HVAC) systems, which consume more

energy than others appliances in a home. HVAC models, such

as a model considering outdoor environment information [8]

and a resistance-capacitance equivalent model considering

building structure [9], for thermal energy management in a

building have been proposed. However, these HVAC models

are highly complicated for use in a HEMS and it is difficult to

obtain the information necessary for the model from the smart

home.This paper presents a reduced HVAC model that can be

applied to the scheduling problem in a HEMS. The proposed

model is based on an ideal model that considers the customer

convenience and characteristics of the HVAC system. For

improving the practicality of the model, parameters related to

indoor heat capacity and heat energy dissipation are estimated

statistically from historical data.

Scheduling problem with energy resource models in a smart

home can be formulated as a mixed integer non-linear

programming (MINLP) problem. The formulation is non-

convex owing to bilinear constraints. The nonlinear, non-

convex nature of the problem affects a significant reduction inthe convergence ratio of the problem and makes it difficult to

arrive at the global optimal solution. In general,

transformation techniques [10]-[12] have been applied to the

original problem for overcoming bilinearity and obtaining a

global optimal solution to the problem. In [13], a

parameterization method was used to the scheduling problem.

Because the method is based on the search tree, the

optimization time of the problem may be longer. Therefore, in

this paper, a linear transformation [12] and optimization

technique is proposed for determining the optimal operating

schedule of the energy resources.

Furthermore, the prototype software based on the proposed

method for HEMs has been developed. In order to determine

the least-cost energy schedules for a smart home, the

implemented software has four modules such as a data input

module, a parameter estimation module, a scheduling module,

and a database management module.

This paper is organized as follows. First, in Section II, a

model considering the HVAC system characteristics and

customer convenience is proposed, and the HEMS scheduling

problem is formulated. In Section III, a method using linear

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H.-C. Jo et al.: Smart Heating and Air Conditioning Scheduling Method Incorporating Customer Convenience for Home Energy Management System 317

transformation and optimization is proposed for solving the

scheduling problem for the HEMS. Section IV describes the

implementation of the prototype software. The numerical

results are analyzed to show the effectiveness of the proposed

method in Section V, and the conclusions of this study are

presented in Section VI.

II. HEATING AND AIR CONDITIONING SCHEDULINGPROBLEM WITH CUSTOMER CONVENIENCE FOR SMART

HOME

A method for the integrated scheduling of all energy

resources in the home is required so that customers can reduce

the overall energy cost through the introduction of a smart home

and time-varying price. Fig. 1 shows the overview of HEMS in

a smart home environment. The energy resources are restricted

to a power grid, photovoltaic (PV) system, ESS, EV, FC, and

HVAC because it is impossible to control all the equipment in a

smart home environment without causing inconvenience to the

customer. In this study, several profiles and parameters are

assumed to be known from forecasts based on the historical data

and information obtained from utility companies.

Fig. 1. Overview of HEMS in a smart home environment.

Models of the equipment such as ESS, FC, and EV that

have been studied with regard to the large-scale power system

or building can be applied to the HEMS. HVAC models

almost similar to a real HVAC system were proposed in [8]

and [9]. However, these HVAC models require additional

information that is difficult to obtain within the home

environment. A reduced and practical HVAC model that can

be applied to a HEMS is required. Therefore, a model that

considers the characteristics of the HVAC system and

customer convenience is used in this study.

A. HVAC ModelIt is difficult for a customer to frequently modify the HVAC

setting for reducing energy cost considering the time-varying

price. Therefore, the HVAC system is assumed to have an

automatic control system that uses the control signal obtained

from the HEMS scheduling module. An HVAC model for

scheduling the operation mode and output by considering the

temperature and time-varying price is proposed.

The utility companies provide the information about the

coefficient of performance (COP) which represents the ratio of

change in heat to supplied electrical demand. Therefore, a

HVAC model is typically expressed as follows:

)()( tpCOPtq HVACHVAC (1)

where qHVAC(t) is the cooling or heating demand generated by

the HVAC system at time t andpHVAC(t)is the electrical demand

of the HVAC system at time t.For estimating the HVAC systems power demand, the

cooling and heating demands should be calculated considering

the COP and temperature in the discrete time domain.

The indoor cooling or heating demand, according to the first

law of thermodynamics, is expressed as follows:

)}()(}{)(){(

)}()1({)(

tTtTtZ

tTtTtq

inoutoffHVACoffon

ininload

(2)

where qload(t)is the cooling or heating demand at time t; Tin(t)isthe indoor temperature at time t; Tout(t) is the outdoortemperature at time t;ZHVAC(t)is a variable denoting the HVAC

system operation at time t; is the indoor heat capacity; and on

andoffare parameters related to the outdoor temperature.The proposed model is considered as ideal if it is applied to

the scheduling problem without correcting the value. For

improving the practicality of the model, the estimated value

must be close to the actual value. For that reason, a method to

update thevalue with the historical data is adopted.

B. Customer ConvenienceFor minimizing overall energy cost, the HVAC system

should not be operated when the home is unoccupied. An

estimate of house occupancy is required for determining the

HVAC operating time. To this end, algorithms based on the

customers manual selection or using sensors have been

commercialized. The nonintrusive load monitoring (NILM)[14]can be used as an alternative method for estimating the time for

which a house remains unoccupied. The NILM is a process for

analyzing changes in the power or voltage supplied to a house,

as shown in Fig. 2, and for detecting the appliances that are used

in the house as well as the total energy consumption.

Fig. 2. Overview of the prototype NILM system.

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If the method based on the manual selection is not used, the

customer occupancy can be statistically estimated by analyzing the

information for the energy consumption of each appliance provided

by the sensors or the algorithm based on the NILM.

Because the temperature in the home decided by the scheduling

may be so high or low that the customer feels inconvenience, the

indoor temperature should be limited to the preferred temperature

established by the customer within the HVAC operating time S.

This requirement can be expressed as follows:

StTtTT in ,)( maxmin (3)

where Tmin and Tmax are the minimum and maximum values,

respectively, of the indoor temperature.

C. Mathematical Model for Scheduling ProblemIn this section, the objective function and constraints for the

home appliances based on the aforementioned model and the

formulation described in [6]-[7], are formulated as follows:

1) Objective functionThe objective of the scheduling problem is to minimize the

overall energy cost incorporating the costs of electricity andnatural gas. The objective function partially considers the

ESS lifetime, and can be expressed as follows:

24

1

)()()()()(

t

bobgasgrid tZBPtpNPtVtEPtpMin (4)

wherepgrid(t)is the power bought from the power grid or soldto the grid at time t; EP(t) is the electricity price at time t;

Vgas(t) is the volume of natural gas used by the FC at time t;

NP is the natural gas price; pb(t) is the power of the ESScharging or discharging at time t;BP is the penalty value forESS discharging based on the Ah-throughput model [15]; and

Zbo(t)is a binary variable denoting the discharging state.

2) Constraints for ESS and EVThe EV model is based on the ESS model, and the

automotive characteristics of the EV are considered.

- SOC dynamicsb

b

Cap

tptSOCtSOC

)()()1( (5)

where SOC(t) is the state of charge (SOC) in the ESS or theEV at time t; and Capbdenotes the capacity of the ESS or theEV.

-ESS and EV capacity limitsmaxmin )( SOCtSOCSOC (6)

where SOCminandSOCmaxare the minimum and the maximumSOC, respectively, of the ESS or the EV.

- Target SOC (only EV)tarfinal SOCtSOC )( (7)

where tfinaldenotes the expected departure time of the EV; and

SOCtaris the target SOC.

-ESS and EV charger limits0],,[],,[)( maxminmaxmin bibibobob PPPPtp (8)

where minbiP andmax

biP are the minimum and the maximum

charging power, respectively, of the ESS or EV charger; and

minboP and

maxboP denote the minimum and the maximum

discharging power of the ESS charger only.

3) FC ConstraintsGiven its role as a small cogeneration unit, the FC generates

heat as a by-product. FC characteristics can be formulated asthe combined heat and power system model proposed in [7].

- Volume of natural gas used by FCnorelec

FCgas

qEftptV

)()( (9)

where pFC(t) is the power generated by the FC; Efelec is the

electrical efficiency of the FC; and qnoris the energy per unitvolume of natural gas.

- Quantity of heat energy generated by FC)(

)1()( tp

Ef

EfEftq FC

elec

elecheatFC

(10)

where qFC(t)is the heat energy generated by the FC at time t;andEfheatis the FCs thermal efficiency.

-FC generation limits0],,[)(

maxmin

FCFCFC PPtp (11)where minFCP and

maxFCP are the minimum and the maximum

power generated by the FC, respectively.

4) HVAC system constraints-Heat balance

)()()( tpCOPtqtq HVACFCload (12)

},{)},()1({

})(){()}()({)( ,,,

FCHVACEtTtT

tZtTtTtq

inin

Ei

offiioffioniinoutload

(13)

where i,onand i,offare the values in the operating state and

non-operating state of the thermal device i, respectively.

- Customer-preferred temperatureStTtTT in ,)( maxmin (14)-HVAC power consumption limits

0],,[)( maxmin HVACHVACHVAC PPtp (15)

where minHVACP andmax

HVACP are the minimum and the maximum

electrical demand of the HVAC system, respectively.

5) Power balance constraints)()()()()()( tptptptptptp loadHVACbFCsolargrid (16)

wherepsolar(t)is the power supplied by the PV at time t; andpload(t)is the total demand from the uncontrolled appliances at time t.

III. SMART HEATING AND AIR CONDITIONINGSCHEDULING METHOD FOR HEMSThe solution to the problem formulated as an MINLP using

the above-described model could be global or local optimum

depending on problem convexity. Constraint (13) is non-

convex owing to the sum of the bilinear terms. Therefore, it is

necessary to verify that the obtained solution is globally

optimal. Owing to the complexity of this process, the problem

must be converted into a convex form through convexification.

A number of methods to transform a non-convex constraint

into a convex one have been discussed. In this study, an easy-

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to-implement linear transformation technique is adopted [11].

Constraint (13) can be rewritten in a linear form using a set

of slack variablesX(t)for the Tin(t)variables as follows:

},{)},()1({)](

)())}(1()(){([)(

2,,

1,,,,

FCHVACEtTtTtX

tXtZtZtTtq

ininioffi

ioni

Ei

ioffiionioutload

(17)

The bilinear term in constraint (13) can be substituted by

the slack variables. Constraints (18)(23) are added to definethe new variables required for the transformation.

))(1()()())(1()( max1,min1, tZKtXtTtZKtX iiinii (18)

)()()()()( max2,min2, tZKtXtTtZKtX iiinii (20)

)()()( max1,min tZKtXtZK iii (21)

))(1()())(1( max2,min tZKtXtZK iii (22)

Ei (23)

whereKminandKmaxare, respectively, the lower and the upper

bounds of Tin(t).

The difference betweenKminandKmaxshould be as small as

possible. For satisfying constraint (14), Kmin should be lower

than Tmin and Kmax should be greater than Tmax. For selectingappropriateKminandKmaxvalues, historical Tin(t)data are used.

Fig. 3 presents the proposed scheduling method for HEMS.

The entire problem is written in a linear form by replacing the

nonconvex constraint (13) with a linear one (17) and by

adding constraints (18)(23). For solving the problem, first,

the profiles and parameters obtained from the forecast

algorithm are entered into the scheduling problem. Kmin,Kmax,

, and HVAC operating time S are estimated from the

historical data. Additionally, the preferred temperature is

selected manually by the customer or from the estimation

results using past selections. The optimal scheduling result can

be obtained by solving the modified formulation.

Fig. 3. The proposed scheduling method for HEMS.

IV. IMPLEMENTATION OF PROTOTYPE SOFTWARE USINGSMART HEATING AND AIR CONDITIONING SCHEDULING

METHOD WITH CUSTOMER CONVENIENCE FOR HEMS

The prototype software using the proposed method has

been developed using a commercially available, cross-

platform optimization suite. It performs optimization for

scheduling of energy resources and manages the scheduling

results. Fig. 4 shows a configuration of the proposed prototype

software for smart heating and air conditioning scheduling

system. The software is composed of four modules that

include a data input module, a parameter estimation module, a

scheduling module, and a database management module.

Fig. 4. Middleware Architecture of the proposed HEMS.

A. Data Input ModuleAs shown in Fig. 5, the module collects the data that is

directly input to the software by the customer or transmitted

from the prediction modules. The customer can enter

information related to the customer convenience for HVAC

and EV into the software through the interface manager. The

rest of the essential information is obtained from theprediction modules. The collected information is stored in the

database.

Fig. 5. Screenshot of data input in the implemented prototype software.

B. Parameter Estimation ModuleThis module is designed to estimate the parameters from the

historical data in the database. As mentioned in the previous

sections, and K values are necessary to solve the proposed

problem. Given the value estimated from the volume and mass

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of indoor air, can be statistically estimated using equation (2)

and historical data, as shown in Fig. 6. The output power of the

device for smart heating and air conditioning, the indoor

temperature, and the outdoor temperature constitute the

historical data. If necessary, can also be estimated using a

constantvalue via a process similar to that shown in Fig. 6.

Fig. 6. estimation procedure for the proposed method for HEMS.

For selecting the appropriate value ofK, the candidates ofKare determined by analyzing the historical data. Furthermore,

the preferred temperature and the candidates are compared to

determine the value of K. The parameters selected by the

estimation engine are transmitted to the scheduling module.

C. Scheduling ModuleThis module is designed to obtain the schedule of the

energy resources using the proposed method and input data. It

is operated when press the solve button. Given that the values

of , , and K are estimated by the parameter estimation

module, optimization based on the branch and bound

algorithm can be performed to obtain the optimal schedule.

D. Database Management ModuleThe input data and the optimal solution from the scheduling

method is stored in the database through database

management module and displayed, as shown in Fig. 7. Data

that can be displayed in the software include the electrical

demand for the uncontrolled appliances, PV output, prices of

various energy resources, HVAC operation, EV charging, FC

operation, and ESS charging/discharging.

Fig. 7. Screenshot for displaying total energy consumption and electric

energy of energy device.

V. NUMERICAL RESULTSFor testing the proposed method, a scenario based on the

data collected from several institutions during winter is

created. A smart home with a volume of 1,145 m 3 is

assumed for the simulation. It is also assumed that two

adult occupants have a full-time job and the unoccupied

period to be statistically estimated is from 08:30 am to

19:00 pm. The energy resources in the smart homeenvironment include the PVs, FCs, ESS, EV, HVAC, and

the uncontrolled appliances. Several parameters of each

device to be assumed are listed in Table I. The preferred

operating time of HVAC and EV charging time to be

assumed in Table I is based on the unoccupied period. The

value of is estimated from the volume and mass of the

indoor air. The historical data for the estimation of are

obtained from the library building.

TABLEI

SPECIFICATION OF ENERGY RESOURCES IN SMART HOME

Energy Resource Specification

Energy storage system

Capacity 10 kWh

Maximum battery

charge/discharge6 kWh

Electric vehicle

Capacity 16.4 kWh

Maximum batterycharge/discharge

3.3 kWh

Charging time 19 pm7 am

Fuel cell

Capacity 1 kW

0.03 (not operated)0.04 (operated)

Efficiency40% (electricity)

45% (heat)

Heating, ventilating, and

air conditioning

COP 2.73

0.03 (not operated)0.55 (operated)

Preferred operating

time of HVAC19 pm 9 am

Preferredtemperature

19C

The several profiles used in this study are shown in Fig. 8.

Fig. 8 (a) shows the electrical demand profile forecasted from

the electrical energy consumption data of the uncontrolled

appliances and the occupied period. Fig. 8 (b) shows the 3-

kWh PV generation profile. For developing the PV generation

profile, the PV output power data to be obtained from the PVcell in the school building is used. The prices of natural gas,

power bought from the utility, power sold to the utility, and

BPare shown in Fig. 8 (c). The electricity price and natural

gas price are, respectively, based on the time-of-use (TOU)

and natural gas rate for the cogeneration of the utility in

America. The BP is determined by using the ESS price and

the amount of discharge during the lifetime. For the

calculation of the amount of ESS discharge during the lifetime,

it is assumed that the ESS lifecycle at 80% depth of discharge

(DOD) is 3,000 cycle.

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Fig. 8. (a) Electrical demand profile, (b) PV generation profile, (c) Prices

profile of various resources.

Based on several parameters and profiles, the smart heating

and air conditioning scheduling method for HEMS provides a

smart home with an optimal schedule for each energy resource.

The total electrical demand incorporating the demands of

HVAC and ESS charging and electrical energy resource

composition are shown in Fig. 9. As can be seen in the figure,

only the FC is operated when natural gas price is lower than

electricity price. For generating profit using a BP lower than

the TOU, the ESS is discharged at 20:00. In fact, during peak

hours, all energy supplying resources are used for decreasing

the amount of energy purchased from the grid.

Fig. 9. Total electrical demand and composition of electrical energy

supplies in smart home.

As shown in Fig. 10, the proposed HVAC model is

successfully applied to the scheduling problem. There is no

preheating in the unoccupied period, during which the

electricity price is at its peak. In contrast, the HVAC system is

operated within the preferred temperature during the occupied

period. Furthermore, owing to constant indoor temperature, as

shown in Fig. 10, it can be observed that the part representing

the indoor temperature dynamics in constraint (13) is ignored,

and the HVAC output power is proportional to the difference

between the outdoor and indoor temperatures.

Fig. 10. Scheduling result for the HVAC operation and the indoor

temperature change at constant preferred temperature in smart home.

Setting different values of the preferred temperature in the

method could have an effect on the overall energy cost. The

cases with several different Tminand Tmaxvalues are compared

to demonstrate the influence of preferred temperature on the

overall energy cost in the smart home environment. The

preferred temperatures in all these cases are listed in Table II.

In cases I, II, and III, the preferred temperature of each case is

respectively 19C, 20C, and 21C. In contrast, in case IV, thepreferred temperature is determined for the indoor temperature

to be in a range of 19C to 21C for preventing customer

inconvenience.

TABLEII

PREFERRED TEMPERATURE

Case Temperature (C)

Case I 19

Case II 20

Case III 21

Case IV 1921

It can be observed from the results shown in Fig. 11 that the

overall energy cost can be reduced by setting the preferred

indoor temperature within a specific range rather than as a

constant value while avoiding inconvenience to the customer.

The overall energy cost in the case IV is respectively 28%,

32%, and 36% lower than other cases. Furthermore, in case of

the constant preferred temperature, the overall energy cost

increases by approximately 3% when the constant preferred

temperature increases by 1C.

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Fig. 11. Comparison of overall energy cost for various preferred

temperatures.

VI. CONCLUSIONThis paper proposes a smart heating and air conditioning

method with customer convenience for HEMS. Optimalscheduling various energy resources in a smart home is

needed to save energy costs. This paper describes a reduced

HVAC model that considers customer convenience and a

method for solving the scheduling problem of the HEMS with

the reduced HVAC model. The HEMS based on the proposed

method can be used to determine the least-cost schedules of

the available energy resources while minimizing

inconvenience to the customer in a smart home environment.

The numerical results show that the HEMS based on the

proposed scheduling method, which can be installed at various

smart homes, has great potential to reduce customers overall

energy costs.

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BIOGRAPHIES

Hyung-Chul Jo received his B.S. degree in the school of ElectricalEngineering from Korea University, Seoul, in 2010. He is currently pursuinghis Ph.D. degree at Korea University. His research interests include smart

home and building energy management systems and large- and small-scale

system optimization.

Sang-Won Kim received his B.S. degree from Seoul National University,

Seoul, Korea in 2002 and M.S. degree from Korea University in 2013. Since2002, he has been with LG Electronics, Seoul, Korea, where he is

currently a Senior Research Engineer in the Future IT R&D Lab.His main research interests include data mining and optimization algorithms

for smart homes and buildings.

Sung-Kwan Joo (M05) received his M.S. and Ph.D. degrees from theUniversity of Washington, Seattle, in 1997 and 2004, respectively. From 2004

to 2006, he was an Assistant Professor in the Department of Electrical andComputer Engineering at North Dakota State University, Fargo, U.S. He is

currently an Associate Professor in the School of Electrical Engineering atKorea University, Seoul, Korea. His research interests include

multidisciplinary research related to energy systems, involving information

technologies, optimization, and intelligent systems.

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