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Smart Home Energy Management

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (30 November 2016) | Viewed by 69565

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Special Issue Editors

Dr. Giovanni Pau

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Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Interests: wireless sensor networks; intelligent transportation systems; Internet of things; green communications; fuzzy logic
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Prof. Dr. Mario Collotta

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Engineering and Architecture Faculty, Kore University of Enna, 94100 Enna, Italy
Interests: expert systems and networks in distributed systems; IoT; real-time and healthcare application
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Prof. Dr. Antonio Ruano

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Faculty of Science & Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
Interests: computational intelligence; intelligent control; energy
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Prof. Dr. Jiahu Qin

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Department of Automation, University of Science and Technology of China, Hefei 230027, China
Interests: consensus and coordination in multi-agent systems; distributed control of large-scale complex systems; complex network theory and its application; security and privacy in cyber-physical systems
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Special Issue Information

Dear Colleagues,

In recent years, smart homes have attracted more interest from the research community. The main reason is that the use of modern automation technology in the home 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, the energy consumption is still higher than actually necessary and users are unable to yield full comfort in their automated homes. For this reason, new solutions and approaches are 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.

We are inviting submissions to a Special Issue of Energies on the subject area of “Smart Home Energy Management”. A smart home 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 the dwelling and also outside, for instance using smart solutions for the management of the external lights and of the garden. This Special Issue solicits the submission of high-quality and unpublished papers that aim to solve open technical problems and challenges typical of smart home energy management systems, integrating novel solutions efficiently, and focusing on performance evaluations and comparisons with existing standards. Both theoretical and experimental studies for typical smart homes scenarios are encouraged. Furthermore, high-quality reviews and survey papers are also welcomed.

Prof. Dr. Giovanni Pau
Prof. Dr. Mario Collotta
Prof. Dr. Antonio Ruano
Prof. Dr. Jiahu Qin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

energy management systems and networks for smart home
improving energy efficiency in smart homes
demand-side applications
distributed wireless sensors for smart energy management
smart grids
deadline-aware application for energy smart home control
energy-aware home area networks
smart metering management systems
time-of-use and real-time pricing applications
green communications for smart home
innovative solutions for eco-homes with greenhouse
smart energy solutions for home and garden
energy saving approaches and solutions
use of alternative energy sources (photovoltaic, wind, )
distributed control and optimization in smart energy management
decision making in smart energy management

Published Papers (10 papers)

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Editorial

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167 KiB

Open AccessEditorial

Smart Home Energy Management

by Giovanni Pau, Mario Collotta, Antonio Ruano and Jiahu Qin

Energies 2017, 10(3), 382; https://0-doi-org.brum.beds.ac.uk/10.3390/en10030382 - 17 Mar 2017

Cited by 44 | Viewed by 7814

Abstract

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.[...] Full article

(This article belongs to the Special Issue Smart Home Energy Management)

Research

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1410 KiB

Open AccessFeature PaperArticle

An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building

by Yaser Imad Alamin, María Del Mar Castilla, José Domingo Álvarez and Antonio Ruano

Energies 2017, 10(3), 321; https://0-doi-org.brum.beds.ac.uk/10.3390/en10030321 - 07 Mar 2017

Cited by 34 | Viewed by 4133

Abstract

The goal of maintaining users’ thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users’ productivity. This paper presents 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. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users’ thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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2667 KiB

Open AccessArticle

Power Consumption Efficiency Evaluation of Multi-User Full-Duplex Visible Light Communication Systems for Smart Home Technologies

by Muhammad Tabish Niaz, Fatima Imdad and Hyung Seok Kim

Energies 2017, 10(2), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/en10020254 - 20 Feb 2017

Cited by 14 | Viewed by 6949

Abstract

Visible light communication (VLC) has recently gained significant academic and industrial attention. VLC has great potential to supplement the functioning of the upcoming radio-frequency (RF)-based 5G networks. It is best suited for home, office, and commercial indoor environments as it provides a high bandwidth and high data rate, and the visible light spectrum is free to use. This paper proposes a multi-user full-duplex VLC system using red-green-blue (RGB), and white emitting diodes (LEDs) for smart home technologies. It utilizes red, green, and blue LEDs for downlink transmission and a simple phosphor white LED for uplink transmission. The red and green color bands are used for user data and smart devices, respectively, while the blue color band is used with the white LED for uplink transmission. The simulation was carried out to verify the performance of the proposed multi-user full-duplex VLC system. In addition to the performance evaluation, a cost-power consumption analysis was performed by comparing the power consumption and the resulting cost of the proposed VLC system to the power consumed and resulting cost of traditional Wi-Fi based systems and hybrid systems that utilized both VLC and Wi-Fi. Our findings showed that the proposed system improved the data rate and bit-error rate performance, while minimizing the power consumption and the associated costs. These results have demonstrated that a full-duplex VLC system is a feasible solution suitable for indoor environments as it provides greater cost savings and energy efficiency when compared to traditional Wi-Fi-based systems and hybrid systems that utilize both VLC and Wi-Fi. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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2125 KiB

Open AccessArticle

The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case

by Alessandro Pitì, Giacomo Verticale, Cristina Rottondi, Antonio Capone and Luca Lo Schiavo

Energies 2017, 10(2), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/en10020199 - 10 Feb 2017

Cited by 95 | Viewed by 10167

Abstract

The Smart Meter (SM) 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 addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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2672 KiB

Open AccessArticle

An Experimental Study of the Impact of Dynamic Electricity Pricing on Consumer Behavior: An Analysis for a Remote Island in Japan

by Thoa Thi Kim Nguyen, Koji Shimada, Yuki Ochi, Takuya Matsumoto, Hiroshi Matsugi and Takao Awata

Energies 2016, 9(12), 1093; https://0-doi-org.brum.beds.ac.uk/10.3390/en9121093 - 20 Dec 2016

Cited by 8 | Viewed by 5814

Abstract

The aim of this research was to investigate how consumer behavior changes after application of dynamic electricity pricing and the persistence of those changes. Based on the investigation results, the authors 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. The dynamic pricing experiment was implemented on Nushima Island, located in the center of Japan, with the participation of 50 households. The methodologies used in this study are panel analysis with random effects, and the difference in differences method. Several linear regression analyses are performed to predict hourly electricity usage from a number of explanatory variables, such as life-style factors, the frequency of access to the visualization website, control for weather factors (wind speed and temperatures), and other attributes of the households to predict the log of hourly electric energy consumption. The results show that dynamic pricing brought about 13.8% reduction of electric energy consumption in comparison with the pre-experiment period. Also, by applying a new experimental design approach, this study finds data supportive of habit formation by participants. Based on the findings, this research tries to develop a policy for sustainable energy conservation in remote islands. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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Open AccessArticle

Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

by Jin Woo Moon, Min Hee Chung, Hayub Song and Se-Young Lee

Energies 2016, 9(12), 1090; https://0-doi-org.brum.beds.ac.uk/10.3390/en9121090 - 20 Dec 2016

Cited by 10 | Viewed by 4361

Abstract

The aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when a building was occupied). The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1) model development; (2) model optimization; and (3) performance evaluation. Two software programs—Matrix Laboratory (MATLAB) and Transient Systems Simulation (TRNSYS)—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature), presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9), hidden layers (3), moment (0.9), and learning rate (0.9). At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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Open AccessEditor’s ChoiceArticle

Experimental Study of 6LoPLC for Home Energy Management Systems

by Augustine Ikpehai, Bamidele Adebisi, Khaled M. Rabie, Russell Haggar and Mike Baker

Energies 2016, 9(12), 1046; https://0-doi-org.brum.beds.ac.uk/10.3390/en9121046 - 12 Dec 2016

Cited by 20 | Viewed by 6081

Abstract

Ubiquitous connectivity is already transforming residential dwellings into smart homes. As citizens continue to embrace the smart home paradigm, a new generation of low-rate and low-power communication systems is required to leverage the mass market presented by energy management in homes. Although Power Line Communication (PLC) technology has evolved in the last decade, the adaptation of PLC for constrained networks is not fully charted. By adapting some features of IEEE 802.15.4 and IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) into power lines, this paper demonstrates a low-rate, low-power PLC system over the IPv6 network (referred to as 6LoPLC), for Home Energy Management System (HEMS) applications. The overall idea is to provide a framework for assessing various scenarios that cannot be easily investigated with the limited number of evaluation hardware available. In this respect, a network model is developed in NS-3 (Version 21) to measure several important characteristics of the designed system and then validated with experimental results obtained using the Hanadu evaluation kits. Following the good agreement between the two, the NS-3 model is utilised to investigate more complex scenarios and various use-cases, such as the effects of impulsive noise, the number of nodes and packet size on the latency and Bit Error Rate (BER) performances. We further demonstrate that for different network and application configurations, optimal data sizes exist. For instance, the results reveal that in order to guarantee 99% system reliability, the HEMS application data must not exceed 64 bytes. Finally, it is shown that with impulsive noise in a HEMS network comprising 50 appliances, provided the size of the payload does not exceed 64 bytes, monitoring and control applications incur a maximum latency of 238.117 ms and 248.959 ms, respectively; both of which are within acceptable limits. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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Open AccessArticle

Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy

by Maytham S. Ahmed, Azah Mohamed, Raad Z. Homod and Hussain Shareef

Energies 2016, 9(9), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/en9090716 - 06 Sep 2016

Cited by 77 | Viewed by 10641

Abstract

Demand response (DR) program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA)-based artificial neural network (ANN) to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM), are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO) based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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863 KiB

Open AccessArticle

Online Air-Conditioning Energy Management under Coalitional Game Framework in Smart Community

by Wei Fan, Nian Liu, Jianhua Zhang and Jinyong Lei

Energies 2016, 9(9), 689; https://0-doi-org.brum.beds.ac.uk/10.3390/en9090689 - 29 Aug 2016

Cited by 7 | Viewed by 4386

Abstract

Motivated by the potential ability of air conditioning (A/C) units in demand response, this paper explores how to utilize A/C units to increase the profit of a smart community. A coalitional game between the households and the load serving entity (LSE) in a smart community is studied, where the LSE joins by selling renewable energy to householders and providing an energy saving service to them through an A/C controller. The A/C controller is designed to reduce the amount of electricity purchased from the main grid by controlling A/C units. An online A/C energy management algorithm is developed, based on Lyapunov optimization, that considers both the A/C energy consumption and the thermal comfort level of consumers. In order to quantify the contribution of A/C units, the Shapley value is adopted for distribution of the reward among the participating householders and the LSE, based on their contribution. The simulation result verifies the effectiveness of the proposed coalitional game for a smart community and the algorithm for A/C. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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923 KiB

Open AccessArticle

An Event-Triggered Online Energy Management Algorithm of Smart Home: Lyapunov Optimization Approach

by Wei Fan, Nian Liu and Jianhua Zhang

Energies 2016, 9(5), 381; https://0-doi-org.brum.beds.ac.uk/10.3390/en9050381 - 19 May 2016

Cited by 37 | Viewed by 7429

Abstract

As an important component of the smart grid on the user side, a home energy management system is the core of optimal operation for a smart home. In this paper, the energy scheduling problem for a household equipped with photovoltaic devices was investigated. An online energy management algorithm based on event triggering was proposed. The Lyapunov optimization method was adopted to schedule controllable load in the household. Without forecasting related variables, real-time decisions were made based only on the current information. Energy could be rapidly regulated under the fluctuation of distributed generation, electricity demand and market price. The event-triggering mechanism was adopted to trigger the execution of the online algorithm, so as to cut down the execution frequency and unnecessary calculation. A comprehensive result obtained from simulation shows that the proposed algorithm could effectively decrease the electricity bills of users. Moreover, the required computational resource is small, which contributes to the low-cost energy management of a smart home. Full article

(This article belongs to the Special Issue Smart Home Energy Management)

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