Adaptive Synthesis of Domestic Home Energy Management in Smart Grid Environment

1. Introduction

Until recent decays, electrical energy efficiency has received less attention from both provider and consumer. However, with the dramatic increase and awareness of energy use concerns, global warming, and the advances in cost-effective technologies, energy efficiency is fast becoming part of real estate management and operations strategy [1].

Energy management while achieving comfort control is of a top priority if a society is trying to control its energy consumption. The electrical power providers were equipped to meet all consumer demands without considering the impact the environment. However, as the electricity demand will continue to put, in the longer term, upward pressure on prices for consumers [2]. These pricing impacts will be felt due to the cost of developing new generation and transmission facilities in order to replace ageing infrastructure [3].

The importance of this topic is growing especially as the consumer demands are increasing exponentially. One of the major problems with the operation of the electrical grid is to maintain, continuously, the balance between the available power and potential demand. The best solution may come from smart use of the energy or/and coordinating different consumers to achieve better management of overall consumption. The utilization of energy during the life span of a building is the most dominating factor in the assessment of the resource consumption of a building. Since buildings are great consumers of energy, efficient utilization of energy is one of the most important means to reach important environmental and climatic goals.

Technology and services can help consumers manage the energy generated by their renewable or traditional electric grid. The advantage of power management is twofold: (1) the consumer consumer may receive benefits in reducing energy bills while having a better guarantee electricity supply, (2) The electrical power provider may optimize its production plan in shaping load curve for example by limiting consumption peaks. In addition, the goal of these smart homes is to maximize occupants’ satisfaction while minimizing operational cost. The integration of smart home systems for an enhanced performance is becoming more and more important to increase consumer satisfaction, efficiently.

Service oriented architecture (SOA) has recently become very popular [4]. There are many advantages of using services oriented architectures in a smart housing. The smart homes have relied to the Service Oriented Architecture (SOA) approach to simplify its design, shorten the development time perform their tasks efficiently. On the syntactic interoperability, it is evident that the SOA could be the ideal solution as defined structure for data exchanges. SOA is very well suited for providing interoperability among heterogeneous systems due to the standard way of data representation and the format is extensible to deal with changing requirements [5].

Generally speaking, A smart home is defined as “at home or working environment, which includes the electronic technology to allow for devices and systems to be controlled automatically” [6].

A smart home is “a dwelling incorporating a communications network that connects the key electrical appliances and services, and allows them to be remotely controlled, monitored or accessed”. Accordingly, a home which is smart must contain the following elements: an internal network, intelligent control and home automation. An internal network is the basis of a smart home, and it can be wired, cable and wireless.

The purpose of the work is to examine the possibilities of energy reduction in smart homes. The utilization of alternative energy sources in energy systems is an important measure to reduce adverse environmental effects emanating from utilization of energy and this subject is studied as well.

The remainder of this paper is organized as follows. In Section II, several efforts on technology related aspects of different evolution of smart home are addressed. The Section III deals with the proposed model. The section IV gives the mathematical formulation of smart energy management. Section V provides different services of a typical smart house as well as realistic home’s amenities to confirm our analysis. Section VI describes the advantages of the proposed system. Section VII concludes the paper with some direction for future works.

2. Evolution of smart home technology

Smart home, digital home, adaptive house, and aware home are terms used to represent future homes. Although these terminologies share much of the common concept of future homes, each terminology has a slightly different emphasis [6].

Connected home emphasizes the connection between digital devices in a home. Smart home focuses on home automation services that can control and administer digital devices locally or remotely [7].

Digital home focuses on sharing digital media such as music, photo, or video and providing digital contents service to homes in liaison with the internet is likely to become easier and cheaper in the near future.

The adaptive and aware homes have been developed to control temperature, heating, and lighting without previous programming by the residents. It continuously monitors the environment and observes the actions taken by the residents (e.g., how they use lights, how they adjust the thermostat). From these data, it infers patterns in the home and uses reinforcement learning, to predict future behavior [8].

Figure 1.

Overview of smart home.

2.1. Electronics devices

Most of the smart homes are equipped with electronic devices. They are categorized into active and passive devices. Active devices include control panels and switches which home occupants will directly interact with and use. Passive devices include sensors, actuators and receivers. An example of a smart home is represented in Fig. 1

A plethora of these devices can be used to increase the human well-being. Example of these devices may include (but not limited): sensor, smart pillows, smart refrigerators, smart thermostats and smart meter.

The sensors can be used in every room to measure temperature, light, humidity etc. The sensors report the data to a central computer, which is associated with thermostat. Based on these readings, the thermostat will communicate with the HVAC (heating, ventilation, and air conditioning system) to adjust the heating of the room according to the preference of the occupant.

Smart pillows are designed to personalize bedtime. They can play people’s favorite music and read bedtime stories. Once an individual begins slipping, the smart pillow can automatically check the quality of sleep and will gradually decrease the music volume and eventually turn off.

These smart refrigerators are capable of keeping track of the individual food items based on their RFID tag. It can track the expiry dates of the food items and also the quantities of food items. It can even prepare a shopping list of the food items to buy once they are consumed.

“Dae-Man et al [7]” discussed residential thermostat. Design and implementation of consumer interfaces tend to be poor in current thermostats. Current plans are to make them compatible with some existing wireless home control systems.

However, there are still several problems related to the modern thermostats. These may include the installation, the technology incompatibilities and the difficulty of understanding or modifying the thermostat control unit. However, the future thermostat will cease being an appendage to the heating and cooling systems and become an independent device of its own, sensing and communicating with a wide range of devices.

An important device in the digital home is a smart meter [10]. The meter stores energy consumption data and transmits them automatically and wirelessly to the utility provider.

The advantage is that nobody needs to go on site to check the meter readings [11]. Thus, a smart home has many benefits like energy conservation, security, safety, centralized control and remote access of home environment.

2.2. Technologies

LAN technologies connect different smart devices at smart home. These technologies can be classified into three main groups: wireless IEEE standards 802. x, wired Ethernet, as well as in-building power line communications like BPL and X10 [12]. Wireless IEEE standards include IEEE 802.11, ZigBee (IEEE 802.15.4), Z-wave and Bluetooth (IEEE 802.15.1) [7-9].

Authors of [13] mention that RFID tags could be attached to all objects of a smart home in order to make various kinds of ubiquitous services possible.

This enables to the occupant to control all the objects having RFID tags through a smart home system. Gateways to manage the systems are needed for intelligent control of smart homes remotely and providing access from the home to external services.

A traditional architecture for smart home is served-centric. To solve the problems caused by server-centric architecture and to support dynamic environment, the authors of [14] proposed a service-oriented architecture (SOA) for smart home environments based on Open Services Gateway Initiative and mobile-agent technology.

3. Proposed system model

3.1. Motivation

In the last century almost everyone uses electric energy every day. The dependency of energy becomes painfully visible when the power goes down.

It has been mentioned that the energy consumption in the residential sector represents 46 % of total energy produced and 23 % of greenhouse gas emissions. Example of a typical distribution of the electrical power consumption in residential home is represented in Fig. 2.

The smart grid has been introduced to increase the connectivity, automation and coordination between these suppliers, consumers and networks that perform either long distance transmission or local distribution tasks. It covers the generation, transmission and distribution of electrical power to consumers (e.g., residents, commercial buildings, industry).

The smart homes branch is a new and unexplored area with future possibilities. The range of services is infinite, but it is hard to make a distinction between the ones that really can be called smart and those that just are ordinary functions [15].

Sophisticated and smart technologies in today’s consumer products make it essential to use the electrical power in residential sectors more efficiency. The Smart House Technologies have in earlier research been put forward as an effective measure to reduce CO2-emissions. A rebound effect is defined as the change in energy demand caused by changes in consumer behavior.

Figure 2.

The distribution of electrical power consumption for a typical residential home.

3.2. System model

As mentioned earlier, the smart home could contain different kind of elements like washer, dryer, HVAC, light, electronics equipment and smart meter. The central computer is able to communicate wirelessly with all these elements directly to the central node. The homeowner on vacation can use a phone to communicate with a central computer in order to arm a home security system, control temperature gauges, switch appliances like a washer on or off, control lighting, central heating and air conditioning, program a home theatre or entertainment system.

Let assume that the home has a set service$\mathrm{S}(\mathrm{i},\dots ,\mathrm{M})$, these services may include cooking, heating, lighting, etc. The problem of task allocation and scheduling is to provide for each service a part of the available energy within a specific time. The goal is to minimize the consumed energy $\mathrm{E}\mathrm{ }(\mathrm{I},\mathrm{ }\mathrm{k})$where $\mathrm{k}\mathrm{ }$is the period of time. The task allocation can be formulated within the framework of control energy in the home. It is calculated using a cooperative method in order to reduce the consumed energy. Within the constraints imposed by both consumer and producer, the interactive energy management for smart home was designed to meet three requirements: (1) cost, (2) comfort and (3) ecology.

Cost Constraint: The smart homes (called also green homes) could derive part or all of their energy from renewable sources. These renewable source energy, such as solar are becoming more widely adopted. Let assume that the home has a set of $I$sources, the energy produced by the source $i$during period $k$is denoted by $E(i, k).$The energy sold to the grid is denoted$\psi$.

The cost constraint is given by:

$${\mathrm{J}}_1=\sum _{i=1}^I\sum _{k=1}^{K}C\left(i.k\right)E\left(i,k\right)-\sum _{k=1}^K\psi \left(k\right)\mathrm{\Xi }(i,k)$$E1

Where $C(i,k)$is the cost of the energy produced by the source, whereas$\mathrm{\Xi }(i,k)$ is the cost of the energy sold to the smart grid.

Comfort constraint: Each service has a satisfaction function called $f\left(S_i\right)$and its weight$\omega \left(i\right)$. The satisfaction criterion of each service is defined by:

$${\mathrm{J}}_2=\sum _{i=1}^I\omega \left(i\right)U\left(S\right(i\left)\right)$$E2

Ecology constraint: The increased focus on the climate challenge in later years has led to an increased push for finding solutions that can decrease $CO2$-emissions and at the same time stimulate economic growth.

Let assumed a set of $I$sources, ${\mathrm{\tau }}_{\mathrm{C}\mathrm{O}2}(\mathrm{i},\mathrm{k})$is the volume of emitted $\mathrm{C}\mathrm{O}2$gas during the period time$\mathrm{ }\mathrm{k}$. The ecology constraint is given by:

$${\mathrm{J}}_3=\sum _{i=1}^I\sum _{k=1}^{K}E\left(i,k\right){\tau }_{CO2}(i,k)$$E3

4. Task scheduling: The key for smart energy management

Traditionally, building control systems have operated based on a homeostatic short-term feedback mechanism. For example, thermostatic control of HVAC components involves typical operations (on/off, change in volume and/or temperature of heating/cooling media, etc.) that are essentially guided by temperature sensing in a space. More recently, building control systems have become increasingly sophisticated. Task scheduling is a common problem in many different applications. However, there is no algorithm that can solve this problem exactly, finding the optimal solution in a reasonable time, for any large configuration of tasks and services [16].

As researchers design and use cooperative systems, they invariably encounter the fundamental question: “when the task should be executed?” in order to cooperatively achieve the global goal. By “task” means a subgoal that is necessary for achieving the overall goal of the system, and that can be achieved independently of other subgoals (i.e., tasks). Tasks can be discrete (e.g., turn on/off the light of the room) or continuous (e.g., monitor the room temperature) and can also vary in a number of other ways, including timescale, complexity, and specificity [17].

Figure 3 shows an example of six different services executed independently over time axis. We adopt an approach to formulate the problem of managing energy in the building in a general form of linear programming by optimizing three criteria: environmental, economic and consumer comfort.

The algorithm is formulated into an integer linear programming (ILP) problem, and then presents a round-up heuristic algorithm which is based on linear programming relaxation.

Figure 3.

Graph of temporal relations.

To evaluate the SOA architecture a web service is used. A Web service describes a collection of operations that are accessible via internet through standardized XML (Extensible Mark-up Language) messaging. Web services use XML to code and decode data and traditionally use SOAP (Simple Object Access Protocol) to transport it using existing protocols, like HTTP (Hypertext Transfer Protocol). Web services technologies are a set of technologies based on XML standards that help describe, access, and interact with web services. Web Services Framework consists of three platform elements:

WSDL (Web service description language), UDDI (Universal Description, Discovery and Integration) and SOAP. Web services technology is a realization of Service-oriented architecture (SOA). When a client wants to use the web service, it searches for the specific service in UDDI. UDDI returns a WSDL file which describes the web service and its location. Clients use this information in the WSDL file to form a SOAP request to the designated computer offering the service. That computer performs the required operation and returns result via SOAP.

The main advantage of web services is its interoperability as it uses a platform and language independent XML technologies. Furthermore, web services can easily be accessed over the internet as it uses SOAP and HTTP for communications. Web services are inexpensive and easy to implement. The objective of this paper is to propose an approach that uses web services to remotely interact with smart home elements to manage energy consumption, in a smart grid environment. The interoperability feature of web services can be used for the integration ofthe electrical elements that are aart of the smart home.

5. Integrated smart home services

To illustrate the results of the energy flows optimization, a typical residential home was selected. The home is composed of 4 bedrooms, a living room, kitchen, and bathroom (Fig. 4).

We assume a smart home with a wireless sensor network based on Zigbee. The smart home contains elements like light or temperature sensors in every room, HVAC (heating, ventilation, and air conditioning), smart appliances, electronics devices (such as TV, radio, DVD players, electronic games, and music players), thermostat and smart meter. Furthermore, there is a central computer that can communicate with all these consumers' elements.

The home’s amenities are presented in Table I and the services are powered by electrical energy. The optimization mechanism must make a plan of allocation of energy resources over 24 hours, the time sampling is ${\mathrm{\Delta }}_{\mathrm{h}}=1$ hour.

Figure 4.

The plan of the prototype smart home.

5.1. Heating service

The heating service is provided by four wall mounted 1KW electric heater, one in each bedroom, and 2k W electric heater in the living room. So, The system has four services ( $S_{rv}\left(1\right)$, $S_{rv}\left(2\right),\mathrm{ }{S}_{rv}\left(3\right),\mathrm{ }{S}_{rv}\left(4\right)$) corresponding to the heating system of the four rooms. The fifth service (i.e, $S_{rv}\left(5\right)$) is corresponding to the heating system of the living room.

Thermal sensation is modeled by the system of inequalities, the optimal temperature range is selected from $T_{min}=18$, $T_{max}=22$. However, the optimal selected temperature was fixed to $T_{opt}=20$. We assume that the home is mainly occupied during two periods of the day. The first period is $[0h,\mathrm{ }8h]$ and the second period is between $[17h,\mathrm{ }24h]$. The criterion of thermal comfort is taken into account when the home is occupied.

Service	Power	Model
Heating	6 KW	Permanent
Hot Water Service	1.5 KW	pre-emptible
Washing Machine	1 KW	pre-emptible
Dishwasher	2 KW	pre-emptible
Cooking	5 KW	pre-emptible
Refrigerator	0.1 KW	Permanent
Television	0.4 KW	No-Permanent
Lighting	1 KW	No-Permanent

Table 1.

Different home’s amenities and services

5.2. Domestic hot water service

The hot water is about 10 to 15% of energy consumption within the residential buildings. The hot water needs also vary depending on the type of building, occupancy and usage patterns of hot water.

The hot water is supplied from the water heater that has a maximum power of 1.5 kW. The demand for water is estimated according to Table 1. Suppose that the home is equipped with a dishwashing, sink, small bathtub. The demand for water in a day is typically around 9 kWh:

The service hot water is modeled by a preemptive service ( $S_{rv}\left(6\right)$) that has a time horizon spanning all day:

$${\mathrm{J}}_1=\sum _{\mathrm{k}=1}^{\mathrm{K}}\mathrm{E}\left(6,\mathrm{k}\right)={\mathrm{E}}_{\mathrm{e}}$$E4

The maximum power consumption constraint of a water heater is:

$$\mathrm{E}\left(6,\mathrm{k}\right)\le \mathrm{ }1500\mathrm{ }\mathrm{W}\mathrm{ }$$E5

5.3. Washing machine service

The service of washing machine ${\mathrm{S}}_{\mathrm{r}\mathrm{v}}\left(7\right)$provided by the washing machine is modeled as a service delay. The energy behavior model of the washing machine is composed of three phases: the phase of heating water, the washing step and spinning phase. It is considered that the washing machine was programmed to cycle 90. The power consumed in the heating phase is 92% of the total energy consumed, 4% of the washing phase and 4% of the phase of the spin. The maximum power consumption in the heating phase is set at 98% of maximum power.

5.4. Dishwasher service

The service of the dishwasher ${\mathrm{S}}_{\mathrm{r}\mathrm{v}}\left(8\right)$ is modeled in the same way that the service of washing machine ${\mathrm{S}}_{\mathrm{r}\mathrm{v}}\left(7\right)$. The operation consists of two different phases:

The water heating phase which represents 70% of total energy consumed. The 30% remaining energy is used for washing using cold water. The typical consumption of a dishwasher is on the order of 2 kWh for 90 minutes.

The formulation of service dishwasher is based on a state automaton with two states, each state corresponding to a service delay.

5.5. The sources of electrical energy

The energy supplier is assumed that the home has a subscription contract with the option of 6kW at peak hours. This service is permanent source ${\mathrm{ }\mathrm{S}}_{\mathrm{r}\mathrm{v}}\left(9\right)$. The capacity constraint source is written as:

$$\mathrm{E}\left(9,\mathrm{k}\right)\le 6\mathrm{ }\mathrm{K}\mathrm{W}$$E6

The purchase cost of electricity is $C\left(9,k\right)$, equal to 11.7 cents/kWh during the peak period, 10.74 cents/kWh during the mid-peak period and 6.54 cents/kWh during off-peak period.

5.6. Local energy production

It is assumed that the consumer is able to install a system of photovoltaic panels with an area equal 100 $m^2$ on the roof of the house. The power generated is 10% of solar energy radiated. This service is considered a local source of intermittent service noted ${\mathrm{S}}_{\mathrm{r}\mathrm{v}}\left(7\right)$.

5.7. The energy sale service

The energy produced by the solar panels could be sold back into the grid when consumption is lower than local production. The consumer could also select a mode that allows reselling the entire power to the grid and importing electricity from grid for local consumption.

Two strategies of using the resource of energy produced by photovoltaic panels are considered. In the first, an efficient way to use this locally produced energy is studied. The balance between consumption and production is written as:

$$\begin{array}{c}\mathrm{E}\left(10,\mathrm{k}\right)+\mathrm{E}\left(9,\mathrm{k}\right)=\mathrm{E}\left(8,\mathrm{k}\right)+\mathrm{E}\left(7,\mathrm{k}\right)+\mathrm{E}\left(6,\mathrm{k}\right)\\ +\mathrm{E}\left(5,\mathrm{k}\right)+\mathrm{E}\left(4,\mathrm{k}\right)+\mathrm{E}\left(3,\mathrm{k}\right)+\mathrm{E}\left(2,\mathrm{k}\right)+\mathrm{E}\left(1,\mathrm{k}\right)\end{array}$$E7

Consequently, energy is obtained from the storage during peak hour and the remaining stored energy is sold back to the grid. The peak hour rate is given as 9.3 cents/kWh and off-peak rate is 4.4 cent/kWh.

5.8. Consumer’s operation modes

According to the consumer’s need; the consumer has the ability to select one of the different modes. In this case, the following optimization criteria are considered:

Consumer comfort Mode: In this mode only the comfort criterion is favored. The consumer comfort mode is often chosen during the period of occupancy. The impact criterion to select this mode is given by:

$$J_1=U\left(1\right)+U\left(2\right)+U\left(3\right)+U\left(4\right)+U\left(5\right)+U\left(6\right)+U\left(7\right)$$E8

Economic Mode: This criterion reflects the cost allocation plan of energy resources which is mainly determined by the cost of energy production. In this mode, the criterion of the cost is favored. The energy management system will seek to shift consumption to off-peak and reduce the consumption of certain services to achieve this goal. To evaluate this mode, the following criterion has been selected.

$${\mathrm{J}}_2=\sum _{\mathrm{k}=1}^{\mathrm{K}=24}\mathrm{C}\left(9,\mathrm{k}\right)\mathrm{*}\mathrm{E}(9,\mathrm{k})$$E9

Green Mode: This mode is intended to promote the reduction of carbon dioxide emissions of the home. The emission of greenhouse gases is calculated based on this criterion. The Co2 minimization is preferred in this mode while maintaining the standard of consumer comfort at a good level. The Electricity from combined heat and power in private houses emits around 200g of CO2 per kWh of electricity produced.

$${\mathrm{J}}_3=\sum _{\mathrm{k}=1}^{\mathrm{K}=24}\mathrm{C}\left(9,\mathrm{k}\right)\mathrm{*}{\mathrm{\tau }}_{\mathrm{C}\mathrm{O}2}(9,\mathrm{k})$$E10

6. Performance evaluation

This paper proposes an approach that utilizes web services technologies to remotely interact with smart home’s elements in a smart grid environment. We assume a smart home that contains a central computer. This computer is the gateway between home elements and the outside world. The web service is implemented on this computer and it is the server for web services.

As described earlier, smart home contains different kind of electronic equipments and home appliances such as: dryer, HVAC, light sensors, temperature sensors, thermostat and smart meter. The central computer is able to communicate wirelessly with all these elements directly or via thermostat.

We assumed that a smart home with a wireless sensor network where the sensors are mounted on the appliances and they are able to run web services. The web server retrieves data from the appliances via the web services running on the sensor nodes. These data can be stored in a database and can be accessed by the user. They showed that their implementation is efficient in terms of running time.

Another facet is concerned with controlling the lighting in each room. The communication network is based on Zigbee interconnected sensor nodes, which pour data into a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management.

A centralized modular energy consumption control system is proposed for efficient and economical comfort control in smart residential spaces. One of the facets is concerned with controlling when the HVAC operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole.

6.1. Web-based graphical consumer interface

To simplify the interaction between consumer and web services, the central computer provides a web interface (see fig. 5).

Figure 5.

Web-based graphical consumer interface [18].

The consumer does not need to invoke the web service directly and therefore does not need to deal with XML-based SOAP and WSDL. All that the consumer needs to do is to access the web based interface over the internet and enter the required information to access or control the smart home elements.

A simulator is used to simulate the elements of a smart home. The previous figure (i.e. Fig 5) illustrates the interaction between consumer and simulator via web services. R1 and R2 refer to a room that has a temperature and light sensor [18].

It is seen that by using web services in this context, the elements in the smart home can be remotely accessed and controlled quickly over the internet. The wireless communications between web services and home elements is within short range as the web services reside in a nearby computer. Therefore, interference and attenuation is reduced.

Currently, smart meters are being deployed to homes in Canada and the United States. Furthermore, TIME-OF-USE (TOU) rates are scheduled to take place in 2012 across Ontario to encourage consumers to shift their loads to off-peak hours.

Consumers are being educated about the advantage of smart meters and TOU rates [19]. A web application, like Google PowerMeter, enables the consumer to view the electricity consumption one day-after. Some energy management applications are also developed [20]. Intel’s “Intelligent Home Energy Management” is a device that can monitor energy consumption in real time. Users can remotely view and control thermostats, appliances and security systems from a mobile phone or PC [21], [22]. Apple Inc. has also developed a “Smart Home Energy management system”. IBM's “Tivoli Monitoring for Energy Management” system provides visibility into key energy consumption and environmental metrics. It also identifies areas where energy consumption can be reduced [23].

6.2. Simulation results

In this subsection, the effectiveness of the proposed model is analyzed. The parameters given in Table I are used for the simulation.

Figure 6 shows that the comfort mode provides a temperature of the living room around 20 degrees (which is defined as an optimal temperature). However, the economic model allows for the used to reduce the electric consumption while keeping the comfort at a good level (the average temperature is still accepted). However, for the green mode, the comfort has deteriorated. The degradation of comfort is the results in a decrease in temperature in the thermal environments.

Figure 6.

Measured temperature of the living room for different user’s modes.

The ecological or green mode provides the lowest consumed power; however this cannot happen without compromising the consumer comfort (Fig. 7).

As expected, the consumer comfort mode generates the largest power consumption. With this mode, the occupants use any service at any time regardless the price of the electricity (off-peak v's on-peak periods). Unfortunately, this model is the mostly adopted by the human nature behavior. A good trade-off between the two modes can be given if the consumer selects the economical mode.

By delaying and by temporizing some service such as dishwasher service to the off-peak, the consumer could reduce both CO2 emission and electricity bill. Nowadays, this ecological behavior becomes increasingly popular.

Figure 7.

The expected power consumption for different user’s modes.

7. Conclusion

The increased focus on the climate challenge in later years has led to an increased push for finding solutions that can decrease CO2-emissions and at the same time stimulate economic growth.

The “smart home” concept is used to explain how domestic tasks can be atomized and new information technology is used to improve the management of the home. This paper explores the feasibility of using interactive automated techniques for energy management in homes powered by renewable technologies.

The simulation was evaluated for typical home with eight different services. Based on simulation, the total consumed electrical power of the smart house could be reduced if the consumer selects the green mode. These results could have a positive impact on consumer behavior.

This work and others are currently investigating to propose an interactive home energy management. This smart system could be built up with equipment and appliances present in a modern home. All appliances could be integrated within the network functionality and they could be added to a centralized server that acts as the “brain” of the system.