Consumers’ behavior on electric power usage
Electricity is the major engine that drives the economy of any country. Consistent and reliable supply of electricity is vital for public and private institutions, and residences as most of the undelaying tasks are power dependents [14]. Reducing the amount of energy used and taking efficient measures can reduce energy consumption and save customers money. This behavioral change is more related to the amount paid against billing costs to private and public users [15]. Consumer behavior is an interdisciplinary research field that employs mainly psychological, sociological and economic theories to assess and predict consumer choices.
In [16], authors state that energy-saving behavior is often influenced by monetary incentive and shall not be generalized into an office building context whereby the users have no financial responsibility on its utility’s expenses. According to the report presented by [17], if the occupants are directly involved in paying the energy bills, their approach in using energy shall be more radical compared to the ones who are not involved in paying the bills. Studies also found that barriers to energy behavioral change may be caused by employees not paying the bills, being unaware of the energy demands of the office, or not seeing any benefit for themselves directly in energy savings [18].
In the literature, social scientist devised different consumers’ behavioral theories and models including theory of planned behavior and psychological determinants of pro-environmental behavior.
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Theory of planned behavior
Theory of Planned Behavior (TPB) consists of three psychological determinants, which are Attitude, Subjective Norm (SN) and Perceived Behavior Control (PBC). Attitude as the first predictor is defined as the beliefs, feelings and action tendencies of a person toward certain issues. It can be described as the subjective judgment of an individual to perform a certain behavior with either positive or negative benefits. Individual intention to perform certain behavior can be influenced by positive attitude [19].
Subjective norm as the second predictor is based on the social pressure from a specific reference group whereby an individual tries to comply. In [16], authors identified six indirect determinants influencing energy-saving behavior such as attitude, subjective norm, habit, perceived behavior control, energy knowledge and motivation. Social Norm is the relevant expectations of a community to which an individual seeks to comply. Personal Norm on the other hand can be explained by three fundamentals: when the individual is aware of the action necessary to solve the issues; when the individual recognizes the action is related to the issues; and when the individual recognizes their own ability to change the situation and condition. PBC as the final predictor refers to the individual’s perception of the difficulty to perform certain behavior and their perceived control over the behavior [20].
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Psychological determinants of pro-environmental behavior
Psychological determinants can be explained as variables that may contribute to fostering individual intention toward energy-saving behavior. According to [21, 22], occupant’s behavior change toward energy conservation can result in greater savings compared to the investment cost made for technological approaches. In the context of energy-saving behavior in institutions, psychological determinants include Attitude, subjective norm, PBC, and Personal Norm [23].
Energy efficiency through change of energy-related behavior represents promising energy savings. Building occupants’ behavior has a great influence on final energy consumption as elaborated in [24]. According to [11], capability, opportunity, and motivation (COM-Behavior) interact to generate behavior that in turn influences these components as shown in Fig. 1. Capability refers to the individual’s psychological and physical capacity to engage in the concerned activity which includes the necessary knowledge and skills. Motivation is related to brain processes that energize and direct behavior, not just goals and conscious decision-making. It includes habitual processes, emotional responding and analytical decision-making. Motivation is the cognitive processes that energize and direct behavior, not just goals and conscious decision-making but also automatic associations and priming. Opportunity, on the other hand, refers to the factors that lie outside the individual but make the behavior possible. The single-headed and double-headed arrows in Fig. 1 represent potential influence between components in the system [11].
Cognitive Internet of things for efficient energy consumption
Currently, the Internet of Things (IoT) technology has attracted the attention of many researchers and is rapidly growing with the business environment. IoT connects physical things like vehicles, buildings, and various devices to the Internet using embedded intelligent sensors and microcontrollers [25, 26]. IoT can be considered as a global network which facilitates communication between human-to-human, human-to-things and things-to-things, by providing unique identity to each and every object.
IoT is the major contributor toward the advancement of smart grid beyond the monitoring and automation. In the field of smart grid, it performs the following tasks [27].
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In Automated Meter Infrastructure (AMI), IoT-enabled smart meters and communication systems enable advanced monitoring up to the consumer end.
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In Supervisory Control and Data Acquisition (SCADA), IoT with Artificial Intelligence (AI) and Machine Learning (ML) allows several advanced functions like multisite integration, predictive maintenance, and fault prevention.
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In Smart inverters, IoT enables solar- or battery-based inverters to manage power flows and respond to grid stability requirements in real time. Rooftop solar inverters when integrated with smart grid applications of the utility can extend their smart functions for multiple purposes.
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It allows remote operation of energy consuming devices to respond to grid requirements and utility pricing signals.
IoT is all about identifying, sensing and communicating technologies with a vision of anytime, anywhere and any media but the communicated data are not foreseen and lack the major decision-making characteristic which is an elementary requirement of smart environment [28]. This requirement triggered the emergence of Cognitive IoT (CIoT) which provides greater improvements in performance and brings in intelligence among the devices in the network. Unlike IoTs, CIoTs interact with network devices with minimum human intervention as the latter integrate the human cognitive into the systems [29]. Figure 2 shows the CIoTs framework and how its four layers interact [29].
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Sensing control layer has a direct interface with the physical environment in which the receptors sense the environment by processing the incoming stimuli and feedback observations to the upper layer and the actuators act to control the perceptors.
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Data-semantic-knowledge layer effectively analyzes the sensing data to form useful semantic and knowledge. In CIoT, semantic means deriving and adopting various technologies from the analyzed information.
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Decision-making layer uses the semantic and knowledge extracted from the lower layer to reason, plan and select the most suitable action from multiple agents to support services for human/social networks and stimulate adaptation to the physical environment. To realize this task, machine learning and microcontrollers are used.
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Service evaluation layer shares important interfaces with social networks in which, on-demand service is enabled to social networks and novel performance metrics are designed to evaluate the provisioned services and feedback the evaluation result to the cognitive process.