Zhou & Yang (2016) - Understanding household energy consumption behavior.pdf

Renewable and Sustainable Energy Reviews 56 (2016) 810 –819

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Understanding household energy consumption behavior: The contribution of energy big data analytics Kaile Zhou a,b,n, Shanlin Yang a,b a b

School of Management, Hefei University of Technology, Hefei 230009, China Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei University of Technology, Hefei 230009, China

a r t i c l e

i n f o

 Article history: Received 3 May 2015 Received in revised form 3 September 2015 Accepted 3 December 2015 Available online 21 December 2015 Keywords: Household energy consumption behavior Energy big data Big data analytics Energy informatics Intervention strategies

a b s t r a c t

Understanding and changing household energy consumption behavior are considered as effective ways to improve energy ef �ciency and promote energy conservation. With the increasing penetration of conventional and emerging information information and communication communication technologies technologies (ICTs) in energy sector, sector, traditional traditional energy energy systems systems are being digitized. The energy big data provides a new way to analyze and understand individuals' energy consumption consumption behavior, and thus to improve energy ef �ciency and promote promote energy conservation. conservation. We �rst propose propose a framewor framework k of the interdis interdiscipli ciplinary nary research research of energy, energy, social and information information science, science, which includes includes energy energy social social science, science, social social informat informatics ics and energy energy informat informatics. ics. Then, different different dimensio dimensions ns and different research paradigms of household energy consumption behavior are presented. Household energy consumption behavior can be analyzed in time dimension, user dimension and spatial dimension. The economic paradigm (including demand response) and the behavior-oriented paradigm (including intervention strateg strategies) ies) are two major major research research streams streams of househo household ld energy energy consump consumption tion behavior behavior.. Finally Finally,, the “4V ” characteristics (i.e., volume, velocity, variety and value) of energy big data are discussed. &  2015 Elsevier Ltd. All rights reserved.

Contents

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Intr Introd oduc ucti tion. on. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 811 Interd Interdisci isciplin plinary ary resea research rch areas areas of of energy, energy, socia sociall and inform informati ation on science science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 811 2.1 2.1. Ener Energy gy soc socia iall scie scienc nce. e. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 812 2.2. 2.2. Soci Social al inf infor orma mati tics cs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 812 2.3. 2.3. Ener Energy gy inf infor orma mati tics cs.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 812 3. Hous Househo ehold ld ener energy gy con consu sump mpti tion on beha behavi vior or . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 812 3.1 3.1. Differe Different nt dimens dimension ionss of househ household old energ energy y consum consumpti ption on behav behavior. ior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 812 3.2. Differe Different nt parad paradigm igmss of resear research ch on househo household ld energy energy consu consumpt mption ion behav behavior ior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813 813 3.2. 3.2.1. Econ Econom omic ic par parad adig igm m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 813 3.2.2 3.2.2.. Beha Behavi vior or-o -orie rient nted ed par parad adig igm m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 814 814 3.3. 3.3. Prac Practi tica call appl applica icati tion ons. s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 815 4. Ener Energy gy big big dat data a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 816 4.1 4.1. Volu Volume. me. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 816 4.2. 4.2. Velo Veloci city ty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 816 4.3. 4.3. Vari Variet ety y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 816 4.4. 4.4. Valu Value e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 816 5. Conc Conclu lusi sion onss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 817 Ackno Acknowl wled edgm gment ents. s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 817 Refer Referenc ences es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817 817

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Corresponding author at: School of Management, Hefei University of Technology, Hefei 230009, China. Tel.: E-mail addresses:  [email protected]  [email protected],,  [email protected]  (K. Zhou).

http://dx.doi.org/10.1016/j.rser.2015.12.001 1364-0321/ &  2015 Elsevier Ltd. All rights reserved.

þ 86 551 6290 4948.

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K. Zhou, S. Yang / Renewable and Sustainable Energy Reviews 56 (2016) 810 –819

1. Introduction

The amount of household energy consumption accounts for a substantial proportion of total energy consumption worldwide. In some European and American countries, the percentage of  household energy use in total energy consumption is approximately 30% [1,2]. In China, the rapid development of economy and society in the past decades has resulted in increasingly high energy demand, in which household energy consumption accounted for a signi �cant proportion [3 –6]. It was reported that residential energy consumption accounted for about 11% of China's total energy consumption in 2012 [7]. As a result, household energy consumption has led to serious environmental problems. For instance, nearly 38% of the total US carbon emissions come from the direct energy use of households in the US  [8]. The energy consumption patterns of different households show high variance, due to the fact that their energy use decision makings are usually affected by various factors. However, household energy consumption has a large saving potential. It is estimated that up to 27% of current households' energy use can be saved through more ef �cient energy use [9]. A case study of  households in EU countries showed that each household can achieve annual savings of 1300 kWh by a combination of technological advances and behavioral changes   [10]. Therefore, understanding and changing the energy consumption behavior of  households are considered as effective ways to improve energy ef �ciency and promote energy conservation  [11]. To mitigate the energy and environmental pressures caused by household energy use, substantial research and development (R&D) efforts on energy ef �cient technologies have been made [12]. Improving energy ef �ciency and reducing energy demand are widely regarded as the most promising, fastest, cheapest and safest ways to mitigate environmental pressures and climate change [13]. Though technological advances and strict regulations are important ways for promoting energy conservation and improving energy ef �ciency   [14], it has been more and more recognized that behavioral factors are of great signi�cance in achieving energy conservation [15–18]. Also, a main objective of  many energy and environmental policies is to achieve households' energy conservation by changing their energy use behavior [19]. It has been suggested that behavioral changes can be just as effective as technological changes   [20]. In the past decades, many behavioral and psychological models of consumers have been developed and adopted in exploring householders' energy consumption behavior and the in�uencing factors [21–25]. Then, different types of intervention strategies were developed, which aimed at stimulating or encouraging changes of people's energy use behavior and thus achieving energy ef �ciency and energy savings [26 –32]. With the increasing penetration of sensing and measurement technology, communication network technology, as well as cloud computing and big data storage and analytics, traditional energy systems are being digitized. Large amounts of energy production and consumption data are generated, collected and stored. This provides the possibility to implement energy big data mining and analysis. Decision support based on advanced data analysis is playing an increasingly important role in the operation and management of energy systems, driving the formation of smart energy systems. Smart grid is a speci �c application form of smart energy system [33–37], in which energy �ow, information �ow and business �ow are integrated [38].  The “smart”  of smart grid means the realization of intelligence of the whole power system, from power generation to consumption. The smart power grid mainly includes six aspects, namely smart power generation, smart power transmission, smart power distribution, smart power transformation, smart scheduling, and smart power use. As one of  the important contents of smart grid, smart power use aims at the

realization of  �exible, ef �cient and personalized electricity consumption, using advanced data acquisition equipment, data analysis techniques, and varied interactive terminals. The energy consumption data in smart grid are mainly collected by the advanced metering infrastructure (AMI)  [39]. Through the smart meters deployed at the user side, large scale electricity consumption data of residential users can be collected in near realtime. Through the ef �cient analysis of electricity consumption data collected by smart meters and other data acquisition terminals, daily or monthly electricity consumption patterns of different users can be discovered  [40]. Understanding the electricity consumption behavioral characteristics of different users is important for both power companies and consumers. For power companies, more timely, �exible and personalized marketing strategies or demand side management measures can be developed  [41]. For electricity consumers, through the real-time interaction with power company, they can adjust and optimize their electricity consumption behaviors, thus reducing their energy costs. The deepening research and development of energy big data analytics and its applications have brought new opportunities for understanding household energy consumption behavior. Energy science, social science and information science are no longer independent disciplines. The large amounts of available energy consumption data and advanced big data analysis techniques have combined to trigger the formation of new interdisciplinary research area, such as energy social science, social informatics and energy informatics. The rest of this paper is structured as follows. Next section provides the interdisciplinary research of energy, social and information science, including energy social science, social informatics, energy informatics and energy social informatics (ESI). Then Section 3  studies the household energy consumption behavior from two aspects, namely the different dimensions and different research paradigms of household energy consumption behavior. Energy big data are brie �y discussed in Section 4. Finally, conclusions are summarized in  Section 5.

2. Interdisciplinary research areas of energy, social and information science

Energy science is no longer an independent discipline. Energy science, social science, and information science intersect one another, thus forming some new crossed research areas.   Fig. 1 shows the intersection of energy, social and information science, as well as the positioning of interdisciplinary research areas, including energy social science, social informatics, energy informatics and energy social informatics (ESI). Information Science

Data & Information

Social Informatics

Energy Informatics

ESI Behavior & Psychology

So ci al Sci en ce

Energy & Environment

En ergy Sci en ce

Energy Social Science Fig. 1.  Interdisciplinary research areas of energy, social and information science.

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 2.1. Energy social science

 2.3. Energy informatics

Research on the energy consumption behavior of consumers is an important way to improve energy ef �ciency and to seek effective energy conservation [12]. The objective of energy social science is to establish behavioral or psychological models to understand energy consumption behaviors and  � nd effective ways to achieve energy ef �ciency and environmental targets. Traditionally, social science has played an important role in energy �eld studies. Social science methodologies and models have been successfully applied in solving many energy and environmental problems. Energy consumption and conservation are important behavioral and psychological research areas [27,42,43] in energy social science. The research works of energy consumption in energy social science have been mainly presented in the following two aspects.

Information and Communication Technologies (ICTs), particularly the emerging information technologies (e.g., big data analytics, internet of things, and cloud computing), are constantly penetrated into the whole process of energy sector from production to consumption   [61]. Traditional energy system is being digitalized, with more intelligence, thus forming the smart energy system. In the smart energy system, large amount of data about energy production or consumption are generated, collected and stored, which can serve as valuable resources to support smart energy management. For instance, the near real-time collection and analysis of individuals' electricity consumption data in smart grid can support the behavioral changes of electricity consumers [62,63]. Information technology is playing an increasingly important role in the  � eld of energy and environmental sustainability [64–67], and information science is gradually integrated with energy science. Energy informatics is an interdisciplinary research �eld of energy science and information science, which applies data and information thinking and skills to increase energy ef �ciency and reduce energy consumption. Watson et al.   [68]   proposed that the objectives of  energy informatics are to increase the ef �ciency of energy demand and supply systems and optimize the energy distribution and consumption networks, through energy data collection, analysis, as well as system designing and implementation. Their core idea was presented as “Energy þ Informationo Energy”. Goebel et al.   [69]   presented an overview of energy informatics and pointed out the scope of energy informatics research, as shown in  Fig. 2. Moreover, energy social informatics (ESI) is a new interdisciplinary research area of energy science, social science and information science. ESI can be de �ned as a research �eld that aims at energy ef �ciency improvement and environmental sustainability using advanced ICTs techniques and behavioral models, from the social and psychological perspectives.

(1) Behavioral and psychological factors underlying individuals' energy consumption behavior. People's energy consumption behavior is affected by many different kinds of factors, including both objective and subjective factors [44–47]. Objective factors are the ones that do not depend on the subjective sense of individuals, such as income levels, housing characteristics, family size, as well as energy prices, climatic conditions, and energy policies. Subjective factors are those related to individuals' intention and awareness. The effects of subjective factors on households' energy consumption behavior are important research questions of energy social science. (2) Intervention strategies (e.g., feedback, goal-setting, information and prompts) that aimed at in�uencing people's energy use behavior and encouraging energy conservation [27]. Generally, there are three types of interventions, namely structural ones (e.g., price policies and subsidies), antecedence ones (e.g., goalsetting, information, and commitment), and consequence ones (e.g., feedback and rewards) [48]. Some research efforts [49–52] have focused on different kinds of feedback devices, and they suggested that speci�c feedback is more effective than generalized ones. To implement effective feedback, Ellegård and Palm [53] pointed out that it is important to understand the actual electricity consumption patterns of individuals. The motivation to reduce energy use was also considered as a key factor in developing feedback strategies [27].  2.2. Social informatics

Social informatics, a.k.a. computational social science, is a crossed research area of social science and information science [54,55]. It represents a research area that “focusing on the relationships between information and communications technologies (ICTs) and the larger social context in which these ICTs exist ”  [56 – 58]. The objective of social informatics research is to deal with social and behavioral issues with data and information. In the age of big data, the large amount multi-source data provides many new research opportunities for social science. Big data analytics can reveal many hidden behavioral patterns of both individuals and groups [59]. The increasingly available informative social science data are making it possible to analyze, interpret, and address many dif �cult social issues  [60]. Here, the purpose of a brief introduction of social informatics is to illustrate the signi �cance of the crossed �elds of energy, social and information science. Though it is an interesting and promising �eld of research, detailed introduction and discussion about social informatics are not provided due to the fact that it is not particularly relevant to the research focus of this study.

3. Household energy consumption behavior   3.1. Different dimensions of household energy consumption behavior 

The energy consumption behavior of households can be described in three dimensions, namely time dimension, user dimension and spatial dimension, as shown in  Fig. 3. In the time dimension, the energy consumption behavior of  households can be in an hour, a day, a month or even a year. With the deployment of smart meters in the demand side of smart grid, householder' electricity consumption data can be collected in near real time. Therefore, householders' energy behavior can be described in different time granularity, from an hour to a year. Generally, the energy use of a household in an hour has great randomness. The energy consumption behaviors of households in a day often show some differences in different times of the day. In contrast, the monthly and annual energy consumption behaviors are usually affected by many external factors. Fig. 4 shows the load pro�le and electricity consumption pro �le of a household in China in a day and in a month, respectively. In the user dimension, the energy consumption behaviors of  different households also vary greatly. Individual's energy use behavior is generally in�uenced by various factors, including both internal and external ones. Internal factors are the subjective intentions, such as habit and environmental awareness. External factors mainly include housing characteristics, demographic characteristics, way of working, and other factors. In addition, the energy consumption behavior of households in spatial dimension also shows certain differences. In different areas,

K. Zhou, S. Yang / Renewable and Sustainable Energy Reviews 56 (2016) 810 –819

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Fig. 2.  Research scope of energy informatics.  Source: [69].

User Dimension In a country In a city In a residential district In a building

Hour

Day

Time Dimension Month

Year  

Different buildings Different residential districts Different cities

Spatial Dimension Fig. 3.  Different dimensions of household energy consumption behavior.

householders' energy uses are often affected by the geographical environment, level of economic development, climate characteristics and other factors. In a smaller spatial range, household energy use behaviors in different residential districts, or even different buildings, also show some differences, due to the impact of regional location, building structure and other spatial differences.  3.2. Different paradigms of research on household energy consumption behavior 

In the past decades, there have been large amounts of research efforts on energy consumption behavior from different perspectives, including behavioral economics   [70], environmental psychology  [71], behavioral and experimental economics  [72], and ecological economics [73]. In summary, the research paradigms of  energy consumption behavior can be divided into two major categories, namely the economic paradigm and the behaviororiented paradigm [74,75].  3.2.1. Economic paradigm The basic principle underlying the economic paradigm is the rational choice theory (a.k.a. the choice theory or the rational action theory), which suggests that people with rationality seek to obtain the maximum bene�t with minimum cost such that to maximize their expected utility [76,77]. In the energy sector, the rational energy consumers make decisions of energy consumption

based on the costs, bene �ts and all of the available internal and external information. From this view, early studies suggested that users would take actions to conserve energy if suf �cient information were provided   [15]. However, the cognitive burden of  information processing always weakens the ability of consumers to take deliberative actions, so that they are bounded in the rationality [78]. Currently, a lot of research efforts have demonstrated that many social and psychological factors like norms, habits and emotions may reduce the cognitive deliberation, thus undermining the assumptions of rational choice theory [74]. From the perspective of economic paradigm, demand response (DR) programs, or in a broader sense demand side management (DSM), are effective ways to promote energy consumption behavioral changes of households through price- or incentive-based strategies. DSM includes the many actions from the replacement of  energy-ef �cient appliances, to the reduction of energy consumption and the shifting of time when electricity is used, to the implementation of complex dynamic pricing mechanisms   [79]. The objective of DSM is to promote behavioral changes of households' energy use. These changes in the time pattern and magnitude of the network load lead to the desired changes in their load shapes. The six major types of DSM objectives and tasks are peak clipping, valley �lling, load shifting, strategic conservation, strategic load growth, and  � exible load shape [80–82]. DR is de �ned as “Changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of  electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized ”  [83,84]. According to this de�nition, DR programs can be divided into two categories, namely price-based DR programs and incentive-based DR programs. Price-based DR programs aim at changing the energy consumption patterns of consumers by different electricity pricing mechanisms [85], while incentive-based DR programs mean the planned changes in electricity consumption that customers have agreed to response to requests from the operators  [86]. Some of  the major DR strategies are shown  Fig. 5. To implement effective DR programs, the collected energy consumption big data are important resources. By the ef �cient analysis of the energy use data, different energy consumption patterns of different households can be discovered, and the corresponding energy use behavioral characteristics can be identi �ed.

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2 1.5    )    W    k    (    d 1   a   o    L 0.5 0

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Hour 

Daily load profile

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   ) 50    h    W    k    ( 40   n   o    i    t   p   m 30   u   s   n   o 20   c   y    t    i   c 10    i   r    t   c   e    l    E 0

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Fig. 4.   Daily load pro�le and monthly electricity consumption pro �le of a household in China. (a) Daily load pro �le. (b) Monthly electricity consumption pro �le.

Fig. 5.   Some major DR strategies.  Source: [79].

 3.2.2. Behavior-oriented paradigm Different from the economic paradigm, the assumption of  behavior-oriented paradigm is that consumers' energy consumption behaviors are usually determined by the complex interplay of  intrapersonal factors, interpersonal factors and external factors [87], as shown in  Fig. 6. Currently, there have been some research efforts that focused on the energy and environment related behaviors from social and psychological perspectives. Also, some different behavioral models have been proposed [21,22], including the theory of planned behavior (TPB)   [23]   and the improved models   [24], the norm activation model (NAM)  [25], as well as the combinational model [26]. The TPB incorporates individual level variables as predictors of behavior. It posits that the behavioral changes of individuals are determined by three major predictors, namely attitudes toward the behavior, perceived behavioral control, and perceptions of  subjective norms [23,88]. These variables together predict behavioral intentions, which in turn predict actual behavior  [89]. The NAM suggests that normative considerations play important roles in affecting energy consumption behaviors. Namely, people are more likely to reduce their energy consumption when they feel morally obliged to do so. van der Werff and Steg  [90] pointed out that a general conceptualization of the NAM that focus on energy use in general predict a range of different energy behaviors. Based on these behavioral models, there are also some studies that focused on the intervention strategies for energy consumption behavior, including goal-setting, feedback, information and

prompts  [27]. The objective of these interventions is to promote energy conservation through either providing rewards that render pro-environmental decisions more attractive [28,29]  or targeting an individual's perceptions, preferences, and abilities in order to induce eco-friendly behavior [30–32]. According to the goalsetting theory [91–94], the energy consumption behavior of individuals is directed by goals that are dif �cult yet realistic. At the same time, the anticipation of attaining a goal has a motivating effect. Feedback is based on the idea that feedback can in�uence behavior since it provides information about some given performance that people have undertaken [95]. Feedback information is mainly the households' energy consumption in terms of energy units and/or monetary values   [27]. Based on the frequency of  information, feedback can be divided into continuous feedback that using a user interface showing the current energy consumption, and non-continuous feedback that providing daily, weekly or monthly information of energy consumption via mail or the Internet. Existing studies have demonstrated that continuous feedback is more effective than non-continuous one in achieving energy conservation [96 –98]. Information aims at providing people with suf �cient understanding of how to achieve a certain objective, thus motivating them to change behaviors [99]. However, it has been found that information is not necessarily in changing householders' energy consumption behavior and promoting energy conservation, though it can result in higher knowledge levels   [27]. The objective of prompts is to give a reminder or encouragement to consumers for changing their energy consumption behavior. For non-complex behaviors, particularly the well-placed and well-timed ones, prompting is considered as the most effective intervention strategy [100]. The various intervention strategies, aiming at improving energy ef �ciency and stimulating energy saving, can be categorized into three major types, namely structural, antecedent, and consequence ones (shown in Fig. 7) [101]. We also summarize some existing research works that focused on different factors that affecting household energy consumption behavior in Table 1. Table 1   demonstrates that there have been a lot of research efforts that focused on the energy consumption behavior of  households. Different types of factors including intrapersonal [4,72,90,102–106], interpersonal   [107–110]   and external ones [48,51,111–119]   that in�uence household energy consumption behavior have been explored. Intrapersonal factors are the internal factors from their own of households, such as attitude, awareness and habit. Interpersonal factors are the social factors come from interpersonal interaction. External factors are the external economic, technological or environmental factors. The research objects of these studies were households from different countries of the world, including European countries (e.g., the UK   [102], Netherlands [114], and Sweden  [51]), Asian countries (e.g., China [109] and Japan [119]), Northern America (e.g., the US  [112]), and

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Fig. 6.  Behavior-oriented research paradigm of energy consumption behavior.

Fig. 7.  Intervention strategies.  Source: [101].

Oceania (e.g., Australia  [103] and New Zealand [115]). We should note that geographical location is also an important regional factor that could have a signi�cant effect on household energy consumption behavior. Table 1 also shows that the data sample sizes of most existing studies were not big enough, which were usually less than 1000. Also, many of the experimental data were survey data collected via face-to-face interviews/communications, or questionnaires based on email, telephone and Internet. Therefore, these traditional research works that have taken very few data into consideration were insuf �cient in scalability. Due to the widespread deployment of advanced metering infrastructure in energy system and the increasing availability of energy consumption data, energy big data analytics provides a new direction for the future research on household energy consumption behavior.  3.3. Practical applications

Existing studies have demonstrated that behavioral factors have signi�cant effects on household energy use. However, effective intervention strategies aiming at stimulating behavioral changes of households can promote great improvement in energy

ef �ciency and reduction in energy consumption. For example, compared with other attitudes, the attitudes of households towards comfort and �nances always correlate most with their energy use [102,120,121]. In addition, due to the fact that people usually have little knowledge about their indirect energy use [122], the extent to which they are aware of the environmental problems and the extent to which they perceive they can contribute to solving the problems can strengthen their feelings of  moral obligation to solve these problems in turn promoting behavioral changes [90,123]. By changing the energy consumption behaviors of households, domestic energy consumption can be reduced by 10–30% [124,125]. These research results, particularly the future research on household energy consumption behavior based on big data analytics, have important practical implications for policies aiming to improve energy ef �ciency and reduce energy consumption. To reduce energy consumption, mitigate climate change and promote sustainable development, a wide range of energy use behaviors of  households need to be changed. The government, local communities and power companies should take more effective measures to encourage householders' behavioral changes, including introducing them more about the relationship between their energy use and

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 Table 1 Some existing studies on factors that in�uence household energy use.

Factors Intrapersonal factors Attitude (emotions, intentions, willingness, etc.) Environmental awareness Habit/lifestyle Demographical factors (age, gender, education, income, number of persons, etc.) Past experience Interpersonal factors Social norms

E xt ernal factors

Data sample size 128 households in the UK [102]; 468 respondents in the Netherlands  [90]; 200 consumers in Australia  [103] 429 households in Libya  [104]; 612 households in the US  [72] 57 Swedish homes [105] 285 households in Greece  [106]

816 residents in China  [4] 447 people in the US  [107]; 323 girls and their parents in the US  [108]; 280 respondents in China  [109] Social in �uence 38 participants in the US  [110] Feedback ( tailored informatio n, ) 10 0 house holds in Sweden [48]; 4 households in Japan  [111]; 28 households in Sweden [51]; 39 participants in the US  [112] Goal-setting 189 households in the Netherlands [26] Home energy visit program 118 households in the UK [113] Technical progress (smart appliances, smart meters, etc.) 77 households from the Netherlands [114]; 9 participants in New Zealand  [115] Geographical factors (area, population, price level, etc.) 30 people in three different types of residential districts of Finland [116]; 560 customers in Austria [117] Climate factors 320 households in the US [118] Financial incentives (monetary rewards, economic bene �ts, 236 households in Japan  [119]; 816 residents in China  [4] etc.)

carbon emissions (or climate change), providing more speci�c information about their energy consumption, as well as encouraging them to replace with more ef �cient appliances. However, the technologies or knowledge provided to households should not be too complex, since people may encounter information overload  [26] or response fatigue [126]. When people have problems or dif �culties in understanding the intervention strategies or incentive mechanisms, they will lose interest in the information and no longer pay attention to the policies and strategies. Also, people's energy consumption behavior is complex and often deviate from traditional decisionmaking and economic theories, so psychology and behavioral economics can play important roles in designing and delivering effective intervention measures [70].

4. Energy big data

Big data in the energy sector, i.e. the energy big data, also have the “4V ”   characteristics, namely volume, velocity, variety, and value [127–129] (shown in Fig. 8). 4.1. Volume

Literally, big data means lots and lots of data. In energy sector, the wide deployment of smart metering devices (e.g., smart meters) created massive amounts of data. For instance, for a distribution network with 1 million smart meters deployed, the amount of electricity consumption data collected in one year is very large. Table 2  shows the amount of electricity consumption data collected by 1 million smart meters in a distribution network within one year [130]. As Table 2 shows, the amount of electricity consumption data collected once every 15 mins by 1 million smart meters within one year will be up to 2920 TB. This presents not only a storage problem, but an analytic problem of making sense of all that data [130]. 4.2. Velocity

Velocity mainly means the speed of energy big data collection, processing and analysis. Different from traditional post-processing type business intelligence and data mining, the collection and processing of energy big data need surprising speed. To support

Volume

Energy Big Data

Velocity

Velocity

Variety Fig. 8. “4V ”  characteristics of energy big data.

 Table 2 Amount of data collected by 1 million smart meters in one year.

Collection frequency Records collected Terabytes collecteda a

1/day 365 million 1.82 TB

1/h 8.75 billion 730 TB

1/30 min 17.52 billion 1460 TB

1/15 min 35.04 billion 2920 TB

Note: Assuming 5 kB per records collected.

the near real-time decision-makings in energy system, the speed of data collection and processing ranges from sub-second to 5- or 15-mins intervals. 4.3. Variety

Energy big data has a high degree of variety. Generally, it is a mix of structured (e.g., the energy consumption data), semistructured (e.g., data exchanged between smart energy management platform and third-party data aggregators using XML, Web services), and unstructured data (e.g., email or SMS noti �cation about energy use, interactions of consumers on social media about their energy use). In addition, there are also some inter-industry data (e.g., electric vehicle-related data) and outside-industry data (e.g., weather data) in the energy big data. These different types of  data all combined will result in a signi �cant increase in the complexity of energy big data applications. 4.4. Value

Energy big data is meaningless unless its value is explored and mined, to support either the business decisions or customer

K. Zhou, S. Yang / Renewable and Sustainable Energy Reviews 56 (2016) 810 –819

services. For energy products and service providers, it helps them to better understand the energy consumption patterns of different consumers and thus to develop more competitive marketing strategies. For customers, the value typically translates to energy savings, operational ef �ciency, and improved visibility into how they are using energy.

5. Conclusions

The amount of household energy use accounts for a substantial proportion of total energy consumption. However, the energy consumption behavioral patterns of different households show high variance, due to the fact that their decision makings about energy use are usually affected by various intrapersonal, interpersonal and external factors. With the increasing penetration of  sensing and measurement technology, communication network technology, as well as cloud computing and big data analytics, traditional energy systems are being digitized. Thus, increasing amount of energy consumption data is collected. Research and development of energy big data analytics and applications have brought new opportunities for understanding household energy consumption behavior. The overall objective of this study is to enhance the understanding of social issues in energy consumption from the information science (or more speci �cally data science) perspective. This study presented a comprehensive review of household energy consumption behavior, including demand response strategies and intervention strategies that aimed at stimulating more ef �cient energy use of households. In the context of smart energy management enabled by advanced ICTs, the increasing penetration and integration of information science and technologies in the research and application of energy consumption are also discussed. This study contributes to the enhancement of people's understanding about household energy consumption behavior, the development of more effective intervention strategies, as well as the achievement of long-term energy conservation and sustainable development.

 Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions. This work is supported by the National Natural Science Foundation of China (No. 71501056), Anhui Provincial Philosophy and Social Science Planning Project (No. AHSKQ2015D42), the Foundation for Innovative Research Groups of the National Natural Science Foundation of  China (No. 71521001), and the Fundamental Research Funds for the Central Universities (JZ2015HGBZ0093).

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