Role of ICT in Energy-Efficient User Behavior and Changing Consumption Patterns

Household energy consumption has increased substantially and is a major contributor to overall energy consumption [27]. In this section, we focus on opportunities presented by ICT in achieving energy efficiency from users’ and households’ perspective.

Technology has evolved significantly over the years, and nowadays we have many available energy-optimised appliances and systems. These are essential for achieving savings and will have a major impact on the demand for energy. The big question still remains, however: Are the users ready to change their behaviors and adapt to the changes? The answer is complex and not straightforward. The issue is not just technical; social and behavioral aspects are included as well. Nonetheless, ICT can play a significant role in minimising the gap between users and their energy-related decisions by making energy consumption information more visible and real-time available.

It is a matter of debate whether ICT is helping to reduce energy consumption. On the one hand, it can increase the efficiency of systems and services. However, it can also affect the energy-efficiency numbers negatively by establishing an ever-increasing number of power-hungry data centres. Laitner et al. estimate that in recent years, for each kilowatt of energy used by ICT equipment, approximately 10 kilowatts is saved through productivity gains and efficiency improvements [28]. The debate is fueled even more by scenarios in which ICT helps to reduce commuting by introducing teleconferencing or by introducing e-mail as opposed to conventional mail and thus helps to make savings in overall energy consumption. According to a report published by Intellect in March 2011 [29], using smart ICT tools in daily activities makes it possible to reduce global emissions by 15%. The possible savings are much bigger than the global footprint of ICT, which is 2%. Thus, ICT is an important driver and an essential enabler for an energy-efficient future society.

User awareness about energy efficiency is increasing, and as the technology advances we predict that in the future it is going to grow further. With the proliferation of the Internet, social media, and global connectivity, it is possible for ICT to influence the decisions of users in all areas of their personal lives—from buying energy-efficient white goods, to installing smart meters and sensors in their homes, to making energy-efficient decisions about their daily lifestyle routines.

The major challenge in this regard is how to make information more visible in a simple and understandable way. For example, there are many situations in which people have positive intentions of reducing energy consumption or choosing green options when purchasing white goods or electricity. Still, they do not take even the simplest measures to reduce their energy demand. A majority of the world’s population gets their

energy-Bit Bang 47 Bit Bang 47 consumption data on a monthly bill and the quantities they look at are the consumed units of electricity for that month and the money they have to pay for those units. ICT can enable users to visualise energy consumption data in a more presentable and fine-grained way so that the users are more concerned about their energy consumption. In addition, with ICT it is also possible to motivate users to reduce their consumption either by using a social context or a personal goal-based context.

In a different context, we can also consider the example of the automated power-saving features that most of our laptops and workstations have. Although the option for saving is there, all too often users are reluctant to use or unaware of how to use such features, either in a home or office environment. All of these issues present an opportunity for ICT to play a major role in enabling users to reduce their energy consumption and get involved in energy-efficient practices.

2.2.1 Research Trends

As presented in the previous section, although there are potential opportunities for energy savings, end users are generally less motivated to save energy, less informed about their energy consumption patterns, and less literate about the energy-saving features of their equipment and devices. Keeping all these deficiencies in mind, substantial research efforts have focused on profiling user behavior for potential areas of improvement, enabling in-home smart technologies for real-time energy data communication, and using machine-to-machine and human-to-machine communication paradigms for improving the energy consumption experience of users and enabling them to make smart decisions related to energy.

Barbato et al. [30] present architecture for intelligent user behavior profiling and automatic system calibration with real-time data using wireless sensor networks (WSNs). They have used WSN to monitor physical parameters such as user presence, light, and temperature.  This architecture makes use of MobiWSN middle-ware, which enables home automation systems to interact with the WSN. Once the aforementioned data are gathered, different profiles are created for lighting, temperature, and user presence. Profiling is performed in both off-line and real-time mode. In a real-time scenario, profiling data are analysed to make smart decisions—for example, regulating the lighting system according to the level of natural light from windows, controlling the heating/air-conditioning system to set the temperature in the rooms according to the user profile, and so forth. Although the system is self-adaptive and automated in most scenarios, user involvement in decision making is rather limited, and as a consequence it might demotivate users from using it.

Mattern et al. have compiled an interesting set of measures [31] for inducing behavioral changes, which are categorised in two groups: rational behavior, which incorporates informational support, and irrational behavior, which constitutes motivational and social positioning measures.  Informational support is concerned with presenting the consumption data to the end user in an understandable and comparative way. It is of no debate that presenting consumption data in mere kilowatts

48 How Can We Change the Everyday Energy Consumption Patterns of Citizens?

or other technical terms is of little help for the majority of the user base. Information should be presented in a simple and understandable way to be adequate for most of the users. Comparative consumption data using other nearby households or families are also useful for a user to perceive his or her own rank among others with similar consumption patterns, whereas the motivational measures are more about instilling energy-efficient decisions in users’ thinking processes. Examples of such measures are goal-setting, energy budgets, and social comparisons. Goal-setting measures help people to set individual goals and can deliver continuous feedback on the current state of consumption and may provide suggestions on ways to improve the consumption pattern to achieve the initial objective or goal.

Measures based on an energy budget motivate the user to conserve more and spend less in order to be within the energy budget at the end of the month, which was set at the beginning of the month. Energy budgets can also be set for daily consumption scenarios. In fact, a British pilot project suggests that with pre-paid electricity tariffs, a method of energy budget–based monitoring proved to be influential and resulted in substantial saving efforts [32].

Mattern et al. [31] proposed an eMeter system, which connects a smart electricity meter with a mobile phone application. This simplistic approach tries to realise promising energy usage feedback features in its system. The eMeter system has three independent components: a smart electricity meter that monitors the total domestic consumption, a gateway that manages and gives access to the logged measurement data, and a portable user interface on a mobile phone that provides real-time feedback on energy consumption. The interface enables users to interactively monitor, measure, and compare their energy consumption with the granularity of device-level data. Figures 3 and 4 present the overall architecture and different user interfaces of the system.

Bonino et al. have performed a thorough investigation and analysis of the capability of a smart home to automatically and timely inform its inhabitants about their energy consumption. With the results of their survey on over a thousand participants, they also analysed and formulated an understanding of what feedback is felt by home inhabitants to be easier to understand, more likely to be used, and more effective in promoting

Fig. 3. Smart meter communicating with the mobile UI [31]

Fig. 4. eMeter user interface (left to right): current consumption view, history view (cumulative consumption), history view (budgeting), device inventory view, measurement view [31]

Bonino et al. have performed a thorough investigation and analysis of the capability of a smart home to automatically and timely inform its inhabitants about their energy consumption. With the results of their survey on over a thousand participants, they also analysed and formulated an understanding of what feedback is felt by home inhabitants to be easier to understand, more likely to be used, and more effective in promoting behavior changes. In addition, they have harvested user opinions on energy feedback interfaces [27]. This research also introduces the concept of different measures like Mattern et al. [31]; Bonino et al. [27] categorise energy-saving strategies as measures based on information, goal-setting, or commitment, and they have used feedback-, reward-, and criticism-based approaches to inform users about their consumption patterns.

Fig. 3. Smart meter communicating with the mobile UI [31]

Bit Bang 49 Bit Bang 49 behavior changes. In addition, they have harvested user opinions on energy feedback interfaces [27]. This research also introduces the concept of different measures like Mattern et al. [31]; Bonino et al. [27] categorise energy-saving strategies as measures based on information, goal-setting, or commitment, and they have used feedback-, reward-, and criticism-based approaches to inform users about their consumption patterns.

Bonino et al.’s [27] visualisation layout as shown in Figure 5 is both appealing and sensible, as they have used colour-based real-time consumption visualisation to display the data. As the energy consumption of any particular room changes, the subsequent colours also reflect the change—for example, a change from green to red informs the user that the consumption in that particular room is higher than normal. This research concludes that most users are interested in having such interactive home displays (IHDs) in their homes and that the goal-based feedback systems are indeed useful.

Although a good amount of research has focused on which type of information is more effective and user-friendly, few scholars have actually involved the end users

Fig. 3. Smart meter communicating with the mobile UI [31]

Fig. 4. eMeter user interface (left to right): current consumption view, history view (cumulative consumption), history view (budgeting), device inventory view, measurement view [31]

Bonino et al. have performed a thorough investigation and analysis of the capability of a smart home to automatically and timely inform its inhabitants about their energy consumption. With the results of their survey on over a thousand participants, they also analysed and formulated an understanding of what feedback is felt by home inhabitants to be easier to understand, more likely to be used, and more effective in promoting behavior changes. In addition, they have harvested user opinions on energy feedback interfaces [27]. This research also introduces the concept of different measures like Mattern et al. [31]; Bonino et al. [27] categorise energy-saving strategies as measures based on information, goal-setting, or commitment, and they have used feedback-, reward-, and criticism-based approaches to inform users about their consumption patterns.

Fig. 4. eMeter user interface (left to right): current consumption view, history view (cumulative consumption), history view (budgeting), device inventory view, measurement view [31]

Fig. 5. Visualisation layout [27]

Bonino et al.’s [27] visualisation layout as shown in Figure 5 is both appealing and sensible, as they have used colour-based real-time consumption visualisation to display the data. As the energy consumption of any particular room changes, the subsequent colours also reflect the change—for example, a change from green to red informs the user that the consumption in that particular room is higher than normal.

This research concludes that most users are interested in having such interactive home displays (IHDs) in their homes and that the goal-based feedback systems are indeed useful.

Although a good amount of research has focused on which type of information is more effective and user-friendly, few scholars have actually involved the end users in this process and asked which type of energy consumption information would be useful and effective for them to visualise the energy-use scenario and conserve energy. Sami Karjalainen presented interesting research in this regard in 2011 [33]. In this paper, the author first developed eight different prototypes that constituted different ways of presenting electricity consumption feedback. While building the prototype, systematical analysis was performed on different types of feedback methods and the prototypes were carefully designed, as claimed by the author. The consumers were shown the prototypes and interviewed regarding their ranking of the prototypes and what they would like to see in addition. The results from this research suggest that consumers would like to see the following features/data in the feedback:

energy consumption cost over a period of time, appliance-specific breakdown of consumption, and historical comparison (i.e., comparison with their own prior consumption). The prototypes are presented in Figure 6.

Fig. 5. Visualisation layout [27]

50 How Can We Change the Everyday Energy Consumption Patterns of Citizens?

in this process and asked which type of energy consumption information would be useful and effective for them to visualise the energy-use scenario and conserve energy.

Sami Karjalainen presented interesting research in this regard in 2011 [33]. In this paper, the author first developed eight different prototypes that constituted different ways of presenting electricity consumption feedback. While building the prototype, systematical analysis was performed on different types of feedback methods and the prototypes were carefully designed, as claimed by the author. The consumers were shown the prototypes and interviewed regarding their ranking of the prototypes and what they would like to see in addition. The results from this research suggest that consumers would like to see the following features/data in the feedback: energy consumption cost over a period of time, appliance-specific breakdown of consumption, and historical comparison (i.e., comparison with their own prior consumption). The prototypes are presented in Figure 6.

In addition to these trends in devices that allow users to get instantaneous feedback about their daily consumption patterns, a significant amount of research is also focused on home automation systems, which are aimed at making the daily lives of users more efficient. The Internet of Things (IoT) or machine-to-machine communication (M2M) allows communication between entities of any kind. Houses and buildings deployed with sensors would allow the use of devices with communication technologies that are able to identify and communicate with each other. Users may enjoy a totally new and energy-efficient environment without needing to worry or think about consumption patterns.

An example is a smart house that is equipped with automation tools and sensors.

Suppose, for example, that the windows have smart blinds that can communicate with the electrical lights and bulbs and lessen their intensity or switch them off totally based on the natural light sensed from the environment. In addition, sensors inside a room can automatically detect the presence of people inside the room and switch the lights on and off automatically. With the help of smart phones, it is possible to operate the home’s cooling or heating system remotely. A smart TV may adjust its brightness or switch on and off based on the presence of viewers or on their attention toward the monitor. GhaffarianHoseini et al. [34] present a number of such examples, which were developed as pilot projects in different universities and show the wide range of possibilities that a home automation system can offer. These methods can provide many plausible solutions for a sustainable, energy-efficient society of the future.