There is no definite date for the origin of smart homes. The very first projects might date back to the 1930s, when pools were equipped with an automatic vacuum system. Also, there is no standard and unified definition for smart homes. However, there are certain keywords that are common in most of the suggested definitions. Different features such as “automation,” “remote control,” and “sensors” are among the prevalent keywords used in these definitions. Various components in smart homes are designed and selected based on the intended service(s). Smart homes can be focused on security, comfort, energy management, etc.
Defining a comprehensive and standard definition for smart homes, identifying common components, recognizing the main services, and reviewing example projects all seem to be needed to facilitate the growth of this topic.
What is a Smart Home?
Dozens of different definitions are provided by researchers that are available in different references. The Oxford online dictionary defines smart homes as “a home equipped with lighting, heating, and electronic devices that can be controlled remotely by smartphone or computer.” This refers to only a limited number of aspects of smart homes, which can be enhanced much further by using the following definition:
A smart home is a dwelling in which data related to a home environment and its residents are obtained from sensors, electric appliances, or a home gateway and transferred through a network of communication tools to a monitoring device or execution unit to help decide on or execute proper actions called services. These services are provided either automatically or directly through a remote or central control system to facilitate or improve the residents’ daily lives.
What are the common services of Smart Homes?
Among the different intended services of smart homes, four major areas can be identified including “health,” “security,” “entertainment,” and “energy management.” There are overlaps among all of these services, and most of the products in the market are focused on these areas. Smart homes for health-oriented services can be equipped with different types of sensors such as pressure sensors that are also referred to as fall detection sensors. Security services are provided by different monitoring and control systems such as contact sensors at doors, which sends an alarm to occupants’ cellphones when the door/window is open while they are away. Most of the voice-activated systems in homes are good examples of the entertainment services of smart homes.
Smart homes with energy management services can be referred to as energy smart homes. The main goal in such homes is to reduce energy use and energy-related costs through different measures.
What are the different types of Energy Smart Homes?
Based on the type and complexity of different components in an energy smart home, they can be categorized under one of the following types:
- Energy Monitoring Systems
- Systems with Control Capabilities
- Systems with Advanced Data-Processing Capabilities
Energy Monitoring Systems
As the name implies, energy monitoring systems are meant to help the user with monitoring energy use. It can provide granular or accumulated data in regard to energy use. For example, in-home displays, which show hourly, monthly, or annual energy consumption, falls under this category. The energy-related data can be monitored on smart devices or a computer through a web-based system. Clamp-on sensors are typically used to measure the electric current through the wires inside the electrical panel board. Providing energy use information encourage the occupants to reduce their energy consumption and researchers have reported 7-15% reduction in energy use after adopting such systems.
Systems with Control Capabilities
The second type refers to the systems that enable the user to have control over different systems or appliances. For example, a programmable thermostat automatically controls the heating and cooling system. Or smart plugs provide the energy use data of different appliances inside the house and let the user switch them on/off remotely through smart devices such as a cellphone.
These systems typically work based on simple “if-then” rules. For example, “if there is a movement, then turn on the light.” The movement is recognized by a motion sensor, and a simple control switch turns the lights on/off. Most Internet of Things (IoT) products fall under this category, where heating, cooling, lighting, and appliances are controlled by simple if-then algorithms. Based on the literature, the energy saving potentials of such systems varies a lot and it can be up to 30% of total annual energy consumption.
Systems with Advanced Data-Processing Capabilities
The third category, however, includes the systems that function through more complicated decision-making processes. There are mostly multiple inputs and outputs in these systems. For example, a building management system that collects the outdoor temperature, solar radiation, and level of occupancy to adjust the flow of refrigerant inside the variable refrigerant flow (VRF) system or to automatically adjust the angle of operable blinds to optimize the daylighting and solar radiation entering the building while maintaining the temperature within the comfort temperature. Such systems achieve their maximum potential when they are connected to smart grids, where a two-way communication with the grid is necessary. The reported energy savings after adopting similar systems varies a lot between different case studies and it can be observed in some cases it has reduced the energy use by 40%.
With the emergence of new technologies such as distributed energy resources (DER) or demand response (DR) systems, where the real-time price of electricity and peak-power periods play an important rule, buildings need to be able to optimize their performance. For example, an energy smart home with advanced data-processing capabilities can collect the environmental data (e.g., temperature, humidity, wind speed, and etc.), the price of electricity, and available solar radiation for solar panels to decide/optimize how much of the energy should come from renewable sources of energy (e.g., solar panels), from the grid, how much of the generated electricity should be stored in the storage system (e.g., lithium-ion batteries or hydrogen fuel cells), and when to operate certain appliances or systems (e.g., heating and cooling systems). These decision-making processes can be based on computer simulation tools, advanced optimization techniques, and machine learning algorithms to provide an accurate model and prediction of the environment and our buildings.