The new challenges on Information and Communication Technologies (ICT) in Automatic Home
Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management
flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or
an outdoor space, equipped with sensors and actuators to collect data and send controls according to
the activities and expectations of the occupants/users. Home automation provides a centralized or
distributed control of electrical appliances. Adding intelligence to the home environment, it would
be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and
outside the dwelling, for instance using smart solutions for the management of the external lights and
of the garden

Collotta, Mario

Pau, Giovanni

Qin, Jiahu

Ruano, Antonio

Energies

English

Giovanni Pau

Mario Collotta

Antonio Ruano

Jiahu Qin

Crossref

Smart Home Energy Management

Sapientia

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/315341999Smart Home Energy ManagementArticle  in  Energies · March 2017DOI: 10.3390/en10030382CITATIONS6READS2294 authors:Some of the authors of this publication are also working on these related projects:Editorials of Special Issues View projectEvent-based networked system View projectGiovanni PauKore University of Enna64 PUBLICATIONS   695 CITATIONS   SEE PROFILEMario CollottaKore University of Enna87 PUBLICATIONS   842 CITATIONS   SEE PROFILEAntonio RuanoUniversidade do Algarve263 PUBLICATIONS   2,337 CITATIONS   SEE PROFILEJiahu QinUniversity of Science and Technology of China123 PUBLICATIONS   1,878 CITATIONS   SEE PROFILEAll content following this page was uploaded by Giovanni Pau on 23 March 2017.The user has requested enhancement of the downloaded file.energiesEditorialSmart Home Energy ManagementGiovanni Pau 1,*, Mario Collotta 1, Antonio Ruano 2,3 and Jiahu Qin 41 Computers Engineering and Networks Laboratory, Faculty of Engineering and Architecture,Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy; mario.collotta@unikore.it2 Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal;aruano@ualg.pt3 IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal4 Department of Automation, University of Science and Technology of China, Hefei 230027, China;jhqin@ustc.edu.cn* Correspondence: giovanni.pau@unikore.it; Tel.: +39-0935-536494Academic Editor: Enrico SciubbaReceived: 15 March 2017; Accepted: 16 March 2017; Published: 17 March 20171. IntroductionThe new challenges on Information and Communication Technologies (ICT) in Automatic HomeSystems (AHS) focus on the methods useful to monitor, control, and optimize the data managementflow and the use of energy. An AHS is a residential dwelling, in some cases with a garden oran outdoor space, equipped with sensors and actuators to collect data and send controls according tothe activities and expectations of the occupants/users. Home automation provides a centralized ordistributed control of electrical appliances. Adding intelligence to the home environment, it wouldbe possible to obtain, not only excellent levels of comfort, but also energy savings both inside andoutside the dwelling, for instance using smart solutions for the management of the external lights andof the garden.Several approaches have been studied in the last years such as advanced metering infrastructure(AMI) [1], smart sensor technologies [2], smart home appliances [3], home area network (HAN) [4]and home energy storage system (HESS) [5]. In general, a Smart Home Energy Management (SHEM)refers to the application of supervisory control and data acquisition with energy management systems,including the generation, the transmission and distribution systems of the electrical network, namelywith the concept of the smart grid. This concept has been widely accepted in order to suggest thefuture development trend of power grids.In this direction, SHEM has become essential for residential customers, such as the home system,for the successful demand-side management of smart grids [6]. SHEM deals with the real-timemonitoring and arranging of various home appliances, based on user’s preferences via intelligentambient systems controlled by a human–machine interface in smart houses, with the aim of electricitycost reduction and energy utilization efficiency improvements [7].SHEM has been developed also to monitor home renewable energies (wind turbines, solar panels,etc.) due to the growing concerns on global energy security and environmental emissions. Coupledwith the rapid development in advanced power electronics and renewable energy technologies,some intelligent systems could be incorporated in SHEM to make the best use of the stored energysources installed at the residential buildings: it would lead the improvement of indoor home efficiencyof energy conversion and utilization [8]. Finally, SHEM is an important challenge to achieving demandresponse, demand side management, peak shaving and the load shifting which are addressed with theaim to offer solutions to the customer or network operators [9].For these reasons, in recent years, SHEM has attracted more interest from the research community.The main reason is that the use of modern automation technology in the home promises considerableEnergies 2017, 10, 382; doi:10.3390/en10030382 www.mdpi.com/journal/energiesEnergies 2017, 10, 382 2 of 5savings of energy, therefore, simultaneously reducing the operational costs of the building over itswhole lifecycle. However, the full potential of smart homes still lies fallow, due to the complexityand diversity of the systems, as well as the frequent problem of suboptimal control strategies. As aconsequence, the energy consumption is still higher than actually necessary and users are unable toyield full comfort in their automated homes. Considering the rapid development of this field in recentyears, several solutions and approaches are thus still needed in order to address the requirementsimposed by the smart home energy management systems, through the development of smart solutions,intelligent algorithms, and novel network paradigms.The goal of this Special Issue was to bring together a wide range of disciplines and technical fieldsto solve open problems and challenges typical of smart home energy management systems, efficientlyintegrating novel solutions, and focusing on performance evaluations and comparisons with existingstandards. In the next section, we provide a brief review of the papers published, roughly classifyingthem according to thematic areas.2. A Review of the Contributions in This Special IssueSeveral papers can be grouped under the broad heading of Energy Management. In fact,a home energy management system is the core of optimal operation for a smart home, representingan important component of the smart grid on the user side.Fan et al. [10] propose an online event-triggering algorithm for energy management of smarthouseholds in order to reduce the electricity cost, with a guarantee of comfort level for householdmembers. The proposed energy management solution can deal with the random demand of consumersand is implemented without user intervention. As a consequence, household members do not needto manually preset the operation time interval of appliances. A Lyapunov optimization methodis adopted in order to schedule the controllable load in the household based only on the currentinformation. Moreover, with the aim to trigger the execution of the online algorithm, so as to cut downthe execution frequency and unnecessary calculation, an event-triggered mechanism is used by theauthors. Simulation results show that the proposed solution could effectively decrease the electricitybill and guarantee the comfort level of users.A theoretical coalitional game approach, for the cooperation between households and load servingentity in a smart community, is presented by Fan et al. in [11]. The authors, considering the potentialdemand response ability of air conditioning units, aim to use them in order to decrease the energycosts of a smart community. The proposed air conditioning controller is designed to reduce theamount of electricity purchased from the main grid by controlling air conditioning units. This ismade possible by an online air conditioning energy management algorithm, based on Lyapunovoptimization, that considers both the air conditioning energy consumption and the thermal comfortlevel of consumers. The obtained results demonstrate that the proposed coalitional game has significantpotential to serve as an effective means of improving the profitability of the load-serving entity andcutting the expenses of householders.Ikpehai et al. [12] present an empirical assessment of low-power Power Line Communication (PLC)system over the IPv6 network (called 6LoPLC) for a home energy management systems. A model isdeveloped in the NS-3 simulator and the network performance is validated with several measurements.The obtained results provide some useful insights for system designers and application developers.Furthermore, the model presented in this paper is also feasible for smart grid applications and othercyber-physical systems, where high reliability and low cost are of higher priority than high throughput.Ahmed et al. [13] show the application of a lightning search algorithm in order to solve theproblems of Artificial Neural Network (ANN) of finding the optimum rate and the best value ofneurons that can be used in a home energy management scheduling controller. In fact, it is known thatdemand response program can shift peak time load to off-peak time, thereby reducing greenhouse gasemissions and allowing energy conservation. For this reason, the home energy management schedulingcontroller of the residential response program strategy, proposed by the authors, aims to predict theEnergies 2017, 10, 382 3 of 5optimal ON/OFF status of home appliances. The obtained results show that the solution proposedby the authors achieve a better response in switching the status in the home energy managementscheduling controller, allowing a substantial energy saving.The aim of Nguyen et al. [14] is to investigate how consumer behavior changes after the applicationof dynamic electricity pricing and the persistence of those changes. They carry out a deep investigationfocused in this research field and also discuss the policy implications of demand management toshift consumption to days that have more solar radiation, while at the same time reducing overallconsumption. To this end, a dynamic pricing experiment is implemented on Nushima Island, locatedin the center of Japan, with the participation of 50 households. The results show that dynamic pricingbrought about 13.8% reduction of electric energy consumption in comparison with the pre-experimentperiod. These results can be useful to develop a policy for sustainable energy conservation inremote islands.Moon et al. [15] have developed an ANN prediction model for controlling building heatingsystems. The main aim is to obtain the ascent time of indoor temperature from the setback period (whena building is not occupied) to a target setpoint temperature (when a building is occupied). The obtainedvalue is applied in order to determine the proper moment to start increasing the temperature from thesetback temperature to reach the target temperature at an appropriate time. The performance of theproposed ANN is widely validated and the obtained results are promising.A smart meter is an essential tool for successful balancing the demand-offer energy curve.It allows the linking of the consumption and production measurements with the time informationand the customer’s identity, enabling the substitution of flat-price billing with smarter solutions,such as Time-of-Use or Real-Time Pricing. In this research field, Pitì et al. [16] review the ongoingsituation in Europe regarding real-time services for the final customers. Moreover, they analyze thearchitectural and technological options that have been considered for the roll-out phase of the Italiansecond generation of smart meters.A new multi-user full-duplex Visible Light Communication (VLC) system to be used in asmart home environment is introduced by Niaz et al. [17]. The proposed system is capable ofaccommodating data for both users and smart devices on downlink while also providing uplinkconnectivity. The authors take into account both the performance of the proposed system and, mainly,the cost and the power consumption. Simulation results show that the solution introduced by theauthors met the requirements of data transmission in an indoor environment such as a smart home.Furthermore, as far as the cost and power consumption, the authors highlight that the initial costof the proposed VLC system is high but when compared to the running cost, which includes thepower consumption and maintenance costs, the proposed VLC system turned out to be a morecost-effective solution.The goal of maintaining users’ thermal comfort conditions in indoor environments may requirecomplex regulation procedures and a proper energy management. For this reason, Alamin et al. [18]present an economic model-based predictive control (MPC) whose main strength is the use of theday-ahead price (DAP) in order to predict the energy consumption associated with the heating,ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a highthermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time,the energy consumption associated with it, as much as possible. The obtained results show that thecontrol system introduced by the authors are able to achieve a tradeoff between users’ thermal comfortand the amount of energy spent to reach this thermal comfort. As a result, the proposed controlstrategy is able to manage the energy spent through the use of the DAP, whereas the users’ thermalcomfort is maximized.3. ConclusionsThis Special Issue highlights the fact that smart homes continue to attract a lot of interest fromthe research community. The main reason is that the use of modern automation technology in homeEnergies 2017, 10, 382 4 of 5scenarios promises considerable savings of energy, therefore, simultaneously reducing the operationalcosts of the building over its whole lifecycle. However, the full potential of smart homes still lies fallow,due to the complexity and diversity of the systems, as well as the frequent problem of suboptimalcontrol strategies. As a consequence, energy consumption is still higher than actually necessary andusers are unable to yield full comfort in their automated homes. To cope with this problem, severalpapers were included in this Special Issue, presenting new solutions and approaches in order toaddress the requirements imposed by the smart home energy management systems. The acceptedpapers introduce smart solutions, intelligent algorithms and novel network paradigms that providea concrete contribution to the literature that deals with the smart home energy management.Acknowledgments: The authors are grateful to the MDPI Publisher for the invitation to act as guest editorsof this special issue and want to thank the editorial staff of Energies for the kind co-operation, patience andcommitted engagement.Author Contributions: The authors contributed equally to this work.Conflicts of Interest: The authors declare no conflict of interest.References1. Ikpehai, A.; Adebisi, B.; Rabie, K.M. Broadband PLC for Clustered Advanced Metering Infrastructure (AMI)Architecture. Energies 2016, 9, 569. [CrossRef]2. Ahmed, M.A.; Kang, Y.C.; Kim, Y.-C. Communication Network Architectures for Smart-House withRenewable Energy Resources. Energies 2015, 8, 8716–8735. [CrossRef]3. Bradac, Z.; Kaczmarczyk, V.; Fiedler, P. Optimal Scheduling of Domestic Appliances via MILP. Energies 2015,8, 217–232. [CrossRef]4. Collotta, M.; Pau, G. A Novel Energy Management Approach for Smart Homes Using Bluetooth Low Energy.IEEE J. Sel. Areas Commun. 2015, 33, 2988–2996. [CrossRef]5. Pascual, J.; Sanchis, P.; Marroyo, L. Implementation and Control of a Residential Electrothermal MicrogridBased on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies 2014, 7,210–237. [CrossRef]6. Collotta, M.; Pau, G. A Solution Based on Bluetooth Low Energy for Smart Home Energy Management.Energies 2015, 8, 11916–11938. [CrossRef]7. Collotta, M.; Pau, G. An Innovative Approach for forecasting of Energy Requirements to improve a SmartHome Management System based on BLE. IEEE Trans. Green Commun. Netw. 2017. [CrossRef]8. Subbiah, R.; Pal, A.; Nordberg, E.K.; Marathe, A.; Marathe, M.V. Energy Demand Model for ResidentialSector: A First Principles Approach. IEEE Trans. Sustain. Energy 2017. [CrossRef]9. Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A. A Hybrid Genetic WindDriven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. Energies 2017, 10,319. [CrossRef]10. Fan, W.; Liu, N.; Zhang, J. An Event-Triggered Online Energy Management Algorithm of Smart Home:Lyapunov Optimization Approach. Energies 2016, 9, 381. [CrossRef]11. Fan, W.; Liu, N.; Zhang, J.; Lei, J. Online Air-Conditioning Energy Management under Coalitional GameFramework in Smart Community. Energies 2016, 9, 689. [CrossRef]12. Ikpehai, A.; Adebisi, B.; Rabie, K.M.; Haggar, R.; Baker, M. Experimental Study of 6LoPLC for Home EnergyManagement Systems. Energies 2016, 9, 1046. [CrossRef]13. Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H. Hybrid LSA-ANN Based Home Energy ManagementScheduling Controller for Residential Demand Response Strategy. Energies 2016, 9, 716. [CrossRef]14. Nguyen, T.T.K.; Shimada, K.; Ochi, Y.; Matsumoto, T.; Matsugi, H.; Awata, T. An Experimental Study of theImpact of Dynamic Electricity Pricing on Consumer Behavior: An Analysis for a Remote Island in Japan.Energies 2016, 9, 1093. [CrossRef]15. Moon, J.W.; Chung, M.H.; Song, H.; Lee, S.-Y. Performance of a Predictive Model for Calculating AscentTime to a Target Temperature. Energies 2016, 9, 1090. [CrossRef]16. Pitì, A.; Verticale, G.; Rottondi, C.; Capone, A.; Lo Schiavo, L. The Role of Smart Meters in Enabling Real-TimeEnergy Services for Households: The Italian Case. Energies 2017, 10, 199. [CrossRef]Energies 2017, 10, 382 5 of 517. Niaz, M.T.; Imdad, F.; Kim, H.S. Power Consumption Efficiency Evaluation of Multi-User Full-Duplex VisibleLight Communication Systems for Smart Home Technologies. Energies 2017, 10, 254. [CrossRef]18. Alamin, Y.I.; Castilla, M.M.; Álvarez, J.D.; Ruano, A. An Economic Model-Based Predictive Control toManage the Users’ Thermal Comfort in a Building. Energies 2017, 10, 321. [CrossRef]© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).View publication stats

Smart home energy management

Universidade do Algarve

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/315341999
Smart Home Energy Management
Article  in  Energies · March 2017
DOI: 10.3390/en10030382
CITATIONS
6
READS
229
4 authors:
Some of the authors of this publication are also working on these related projects:
Editorials of Special Issues View project
Event-based networked system View project
Giovanni Pau
Kore University of Enna
64 PUBLICATIONS   695 CITATIONS   
SEE PROFILE
Mario Collotta
Kore University of Enna
87 PUBLICATIONS   842 CITATIONS   
SEE PROFILE
Antonio Ruano
Universidade do Algarve
263 PUBLICATIONS   2,337 CITATIONS   
SEE PROFILE
Jiahu Qin
University of Science and Technology of China
123 PUBLICATIONS   1,878 CITATIONS   
SEE PROFILE
All content following this page was uploaded by Giovanni Pau on 23 March 2017.
The user has requested enhancement of the downloaded file.
energies
Editorial
Smart Home Energy Management
Giovanni Pau 1,*, Mario Collotta 1, Antonio Ruano 2,3 and Jiahu Qin 4
1 Computers Engineering and Networks Laboratory, Faculty of Engineering and Architecture,
Kore University of Enna, Cittadella Universitaria, Enna 94100, Italy; mario.collotta@unikore.it
2 Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal;
aruano@ualg.pt
3 IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
4 Department of Automation, University of Science and Technology of China, Hefei 230027, China;
jhqin@ustc.edu.cn
* Correspondence: giovanni.pau@unikore.it; Tel.: +39-0935-536494
Academic Editor: Enrico Sciubba
Received: 15 March 2017; Accepted: 16 March 2017; Published: 17 March 2017
1. Introduction
The new challenges on Information and Communication Technologies (ICT) in Automatic Home
Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management
flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or
an outdoor space, equipped with sensors and actuators to collect data and send controls according to
the activities and expectations of the occupants/users. Home automation provides a centralized or
distributed control of electrical appliances. Adding intelligence to the home environment, it would
be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and
outside the dwelling, for instance using smart solutions for the management of the external lights and
of the garden.
Several approaches have been studied in the last years such as advanced metering infrastructure
(AMI) [1], smart sensor technologies [2], smart home appliances [3], home area network (HAN) [4]
and home energy storage system (HESS) [5]. In general, a Smart Home Energy Management (SHEM)
refers to the application of supervisory control and data acquisition with energy management systems,
including the generation, the transmission and distribution systems of the electrical network, namely
with the concept of the smart grid. This concept has been widely accepted in order to suggest the
future development trend of power grids.
In this direction, SHEM has become essential for residential customers, such as the home system,
for the successful demand-side management of smart grids [6]. SHEM deals with the real-time
monitoring and arranging of various home appliances, based on user’s preferences via intelligent
ambient systems controlled by a human–machine interface in smart houses, with the aim of electricity
cost reduction and energy utilization efficiency improvements [7].
SHEM has been developed also to monitor home renewable energies (wind turbines, solar panels,
etc.) due to the growing concerns on global energy security and environmental emissions. Coupled
with the rapid development in advanced power electronics and renewable energy technologies,
some intelligent systems could be incorporated in SHEM to make the best use of the stored energy
sources installed at the residential buildings: it would lead the improvement of indoor home efficiency
of energy conversion and utilization [8]. Finally, SHEM is an important challenge to achieving demand
response, demand side management, peak shaving and the load shifting which are addressed with the
aim to offer solutions to the customer or network operators [9].
For these reasons, in recent years, SHEM has attracted more interest from the research community.
The main reason is that the use of modern automation technology in the home promises considerable
Energies 2017, 10, 382; doi:10.3390/en10030382 www.mdpi.com/journal/energies
Energies 2017, 10, 382 2 of 5
savings of energy, therefore, simultaneously reducing the operational costs of the building over its
whole lifecycle. However, the full potential of smart homes still lies fallow, due to the complexity
and diversity of the systems, as well as the frequent problem of suboptimal control strategies. As a
consequence, the energy consumption is still higher than actually necessary and users are unable to
yield full comfort in their automated homes. Considering the rapid development of this field in recent
years, several solutions and approaches are thus still needed in order to address the requirements
imposed by the smart home energy management systems, through the development of smart solutions,
intelligent algorithms, and novel network paradigms.
The goal of this Special Issue was to bring together a wide range of disciplines and technical fields
to solve open problems and challenges typical of smart home energy management systems, efficiently
integrating novel solutions, and focusing on performance evaluations and comparisons with existing
standards. In the next section, we provide a brief review of the papers published, roughly classifying
them according to thematic areas.
2. A Review of the Contributions in This Special Issue
Several papers can be grouped under the broad heading of Energy Management. In fact,
a home energy management system is the core of optimal operation for a smart home, representing
an important component of the smart grid on the user side.
Fan et al. [10] propose an online event-triggering algorithm for energy management of smart
households in order to reduce the electricity cost, with a guarantee of comfort level for household
members. The proposed energy management solution can deal with the random demand of consumers
and is implemented without user intervention. As a consequence, household members do not need
to manually preset the operation time interval of appliances. A Lyapunov optimization method
is adopted in order to schedule the controllable load in the household based only on the current
information. Moreover, with the aim to trigger the execution of the online algorithm, so as to cut down
the execution frequency and unnecessary calculation, an event-triggered mechanism is used by the
authors. Simulation results show that the proposed solution could effectively decrease the electricity
bill and guarantee the comfort level of users.
A theoretical coalitional game approach, for the cooperation between households and load serving
entity in a smart community, is presented by Fan et al. in [11]. The authors, considering the potential
demand response ability of air conditioning units, aim to use them in order to decrease the energy
costs of a smart community. The proposed air conditioning controller is designed to reduce the
amount of electricity purchased from the main grid by controlling air conditioning units. This is
made possible by an online air conditioning energy management algorithm, based on Lyapunov
optimization, that considers both the air conditioning energy consumption and the thermal comfort
level of consumers. The obtained results demonstrate that the proposed coalitional game has significant
potential to serve as an effective means of improving the profitability of the load-serving entity and
cutting the expenses of householders.
Ikpehai et al. [12] present an empirical assessment of low-power Power Line Communication (PLC)
system over the IPv6 network (called 6LoPLC) for a home energy management systems. A model is
developed in the NS-3 simulator and the network performance is validated with several measurements.
The obtained results provide some useful insights for system designers and application developers.
Furthermore, the model presented in this paper is also feasible for smart grid applications and other
cyber-physical systems, where high reliability and low cost are of higher priority than high throughput.
Ahmed et al. [13] show the application of a lightning search algorithm in order to solve the
problems of Artificial Neural Network (ANN) of finding the optimum rate and the best value of
neurons that can be used in a home energy management scheduling controller. In fact, it is known that
demand response program can shift peak time load to off-peak time, thereby reducing greenhouse gas
emissions and allowing energy conservation. For this reason, the home energy management scheduling
controller of the residential response program strategy, proposed by the authors, aims to predict the
Energies 2017, 10, 382 3 of 5
optimal ON/OFF status of home appliances. The obtained results show that the solution proposed
by the authors achieve a better response in switching the status in the home energy management
scheduling controller, allowing a substantial energy saving.
The aim of Nguyen et al. [14] is to investigate how consumer behavior changes after the application
of dynamic electricity pricing and the persistence of those changes. They carry out a deep investigation
focused in this research field and also discuss the policy implications of demand management to
shift consumption to days that have more solar radiation, while at the same time reducing overall
consumption. To this end, a dynamic pricing experiment is implemented on Nushima Island, located
in the center of Japan, with the participation of 50 households. The results show that dynamic pricing
brought about 13.8% reduction of electric energy consumption in comparison with the pre-experiment
period. These results can be useful to develop a policy for sustainable energy conservation in
remote islands.
Moon et al. [15] have developed an ANN prediction model for controlling building heating
systems. The main aim is to obtain the ascent time of indoor temperature from the setback period (when
a building is not occupied) to a target setpoint temperature (when a building is occupied). The obtained
value is applied in order to determine the proper moment to start increasing the temperature from the
setback temperature to reach the target temperature at an appropriate time. The performance of the
proposed ANN is widely validated and the obtained results are promising.
A smart meter is an essential tool for successful balancing the demand-offer energy curve.
It allows the linking of the consumption and production measurements with the time information
and the customer’s identity, enabling the substitution of flat-price billing with smarter solutions,
such as Time-of-Use or Real-Time Pricing. In this research field, Pitì et al. [16] review the ongoing
situation in Europe regarding real-time services for the final customers. Moreover, they analyze the
architectural and technological options that have been considered for the roll-out phase of the Italian
second generation of smart meters.
A new multi-user full-duplex Visible Light Communication (VLC) system to be used in a
smart home environment is introduced by Niaz et al. [17]. The proposed system is capable of
accommodating data for both users and smart devices on downlink while also providing uplink
connectivity. The authors take into account both the performance of the proposed system and, mainly,
the cost and the power consumption. Simulation results show that the solution introduced by the
authors met the requirements of data transmission in an indoor environment such as a smart home.
Furthermore, as far as the cost and power consumption, the authors highlight that the initial cost
of the proposed VLC system is high but when compared to the running cost, which includes the
power consumption and maintenance costs, the proposed VLC system turned out to be a more
cost-effective solution.
The goal of maintaining users’ thermal comfort conditions in indoor environments may require
complex regulation procedures and a proper energy management. For this reason, Alamin et al. [18]
present an economic model-based predictive control (MPC) whose main strength is the use of the
day-ahead price (DAP) in order to predict the energy consumption associated with the heating,
ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high
thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time,
the energy consumption associated with it, as much as possible. The obtained results show that the
control system introduced by the authors are able to achieve a tradeoff between users’ thermal comfort
and the amount of energy spent to reach this thermal comfort. As a result, the proposed control
strategy is able to manage the energy spent through the use of the DAP, whereas the users’ thermal
comfort is maximized.
3. Conclusions
This Special Issue highlights the fact that smart homes continue to attract a lot of interest from
the research community. The main reason is that the use of modern automation technology in home
Energies 2017, 10, 382 4 of 5
scenarios promises considerable savings of energy, therefore, simultaneously reducing the operational
costs of the building over its whole lifecycle. However, the full potential of smart homes still lies fallow,
due to the complexity and diversity of the systems, as well as the frequent problem of suboptimal
control strategies. As a consequence, energy consumption is still higher than actually necessary and
users are unable to yield full comfort in their automated homes. To cope with this problem, several
papers were included in this Special Issue, presenting new solutions and approaches in order to
address the requirements imposed by the smart home energy management systems. The accepted
papers introduce smart solutions, intelligent algorithms and novel network paradigms that provide
a concrete contribution to the literature that deals with the smart home energy management.
Acknowledgments: The authors are grateful to the MDPI Publisher for the invitation to act as guest editors
of this special issue and want to thank the editorial staff of Energies for the kind co-operation, patience and
committed engagement.
Author Contributions: The authors contributed equally to this work.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Ikpehai, A.; Adebisi, B.; Rabie, K.M. Broadband PLC for Clustered Advanced Metering Infrastructure (AMI)
Architecture. Energies 2016, 9, 569. [CrossRef]
2. Ahmed, M.A.; Kang, Y.C.; Kim, Y.-C. Communication Network Architectures for Smart-House with
Renewable Energy Resources. Energies 2015, 8, 8716–8735. [CrossRef]
3. Bradac, Z.; Kaczmarczyk, V.; Fiedler, P. Optimal Scheduling of Domestic Appliances via MILP. Energies 2015,
8, 217–232. [CrossRef]
4. Collotta, M.; Pau, G. A Novel Energy Management Approach for Smart Homes Using Bluetooth Low Energy.
IEEE J. Sel. Areas Commun. 2015, 33, 2988–2996. [CrossRef]
5. Pascual, J.; Sanchis, P.; Marroyo, L. Implementation and Control of a Residential Electrothermal Microgrid
Based on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies 2014, 7,
210–237. [CrossRef]
6. Collotta, M.; Pau, G. A Solution Based on Bluetooth Low Energy for Smart Home Energy Management.
Energies 2015, 8, 11916–11938. [CrossRef]
7. Collotta, M.; Pau, G. An Innovative Approach for forecasting of Energy Requirements to improve a Smart
Home Management System based on BLE. IEEE Trans. Green Commun. Netw. 2017. [CrossRef]
8. Subbiah, R.; Pal, A.; Nordberg, E.K.; Marathe, A.; Marathe, M.V. Energy Demand Model for Residential
Sector: A First Principles Approach. IEEE Trans. Sustain. Energy 2017. [CrossRef]
9. Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A. A Hybrid Genetic Wind
Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. Energies 2017, 10,
319. [CrossRef]
10. Fan, W.; Liu, N.; Zhang, J. An Event-Triggered Online Energy Management Algorithm of Smart Home:
Lyapunov Optimization Approach. Energies 2016, 9, 381. [CrossRef]
11. Fan, W.; Liu, N.; Zhang, J.; Lei, J. Online Air-Conditioning Energy Management under Coalitional Game
Framework in Smart Community. Energies 2016, 9, 689. [CrossRef]
12. Ikpehai, A.; Adebisi, B.; Rabie, K.M.; Haggar, R.; Baker, M. Experimental Study of 6LoPLC for Home Energy
Management Systems. Energies 2016, 9, 1046. [CrossRef]
13. Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H. Hybrid LSA-ANN Based Home Energy Management
Scheduling Controller for Residential Demand Response Strategy. Energies 2016, 9, 716. [CrossRef]
14. Nguyen, T.T.K.; Shimada, K.; Ochi, Y.; Matsumoto, T.; Matsugi, H.; Awata, T. An Experimental Study of the
Impact of Dynamic Electricity Pricing on Consumer Behavior: An Analysis for a Remote Island in Japan.
Energies 2016, 9, 1093. [CrossRef]
15. Moon, J.W.; Chung, M.H.; Song, H.; Lee, S.-Y. Performance of a Predictive Model for Calculating Ascent
Time to a Target Temperature. Energies 2016, 9, 1090. [CrossRef]
16. Pitì, A.; Verticale, G.; Rottondi, C.; Capone, A.; Lo Schiavo, L. The Role of Smart Meters in Enabling Real-Time
Energy Services for Households: The Italian Case. Energies 2017, 10, 199. [CrossRef]
Energies 2017, 10, 382 5 of 5
17. Niaz, M.T.; Imdad, F.; Kim, H.S. Power Consumption Efficiency Evaluation of Multi-User Full-Duplex Visible
Light Communication Systems for Smart Home Technologies. Energies 2017, 10, 254. [CrossRef]
18. Alamin, Y.I.; Castilla, M.M.; Álvarez, J.D.; Ruano, A. An Economic Model-Based Predictive Control to
Manage the Users’ Thermal Comfort in a Building. Energies 2017, 10, 321. [CrossRef]
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The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy.[...

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