Impact of Personal Factors to Energy Savings, Research Paper Example

Abstract

Change in behavior is becoming a critical exploration project to reduce consumption of energy. More plans concerning environmental behavior study are being done, thus offering ground-breaking energy consumption techniques and tools. This paper provides an outline of energy consumption in behavior change of the population in the United Kingdom. The study determines to analyze research related to energy consumption in households. The empirical study illustrates the measures aimed at the repetitive and constant procedure changes such as feedback on energy consumption, disclosure of information, target setting and utilization of energy in households. Mathematical models on energy intensity and energy elasticity have been applied to determine the level of energy consumption. A variety of methodological limitations were encountered during the study. For instance, only a few types of instruments and mathematical models are used.

Moreover, some of the instruments could not differentiate their impact on energy saving and utilization. Research reveals that getting consistent and useful feedback on the behavior of energy consumption, grant individuals an opportunity to change their behavior. Establishment of energy-saving possibilities in changing the behavior of people; it can be deduced that the practice of people about energy consumption influences the number of personal and macro-level aspects. The individual factors entail particular human abilities, motivation, believes and attitudes norms, knowledge and skills and habits.

Key Words: Energy Consumption

Introduction

The concern of utilizing energy efficiency exists across homes within the United Kingdom [1]. Conserving energy and optimally utilizing energy are structured research and industrial level concern [2, 3]. Energy cannot just be preserved; it could be used for heating other households. In winter, the heat is observed to escape through the poorly sealed windows. In summertime, the sunlight enters the house and causes the air conditioners to pump harder. The use of light in hallways and television adds to the burden. These inefficiencies are in individual homes. These add to the cumulative expenses of energy. The energy is mostly generated by burning coal or by the use of fossil fuels. These are then sent to homes across the nation. The energy inefficiency occurs in these cases where the energy is wasted in mere dissemination or based its use. Wasted energy means that fossil fuels and materials are difficult to extract or produce. The most crucial aspect to the consumers is the wasted energy that translates into more money [4]. These impact the economy of the nation. From this aspect, it can be comprehended that residential reduction in energy expenditure will go a long way to conserving energy and reducing adverse impacts for households.

While many groups are concerned with the improvement of the energy efficiency, such as people from all levels of the government, non-profit groups and the power companies, the government still faces a lot of daunting challenges in this area, because of the need to improve residential energy use in a more custom fashion. Residences are different and social needs concerning energy are incompatible. For this, the government offers incentives to decrease energy usage in families [5]. The role of the individual and their behavior has become the focus of governments and industry alike.  Studies on energy conservation within residences focus on the functioning of the person in the context of energy conservation. Human nature has drawn excellent research attention in the area of energy innovation. Both power companies and engineers are now trying to understand human behavior that shapes energy consumption and how users can be encouraged to reduce energy consumption from social sciences [6]. While human behavior dictates choices when it comes to energy conservation, information to control or improve behavior is an important parameter. Energy studies conducted by economists from the University of California indicates that when a consumer is uninformed, then they could reduce energy usage when price increases by only around 7 percent [7]. Compared to this situation, when a consumer is informed about how their energy needs are more, it would be able to conserve more energy. Hence, it is imperative to research on a social as well as technical scale [8, 9]. The social scale would detail how consumer behavior in energy conservation would be dictated by information sharing within the social network [10]. The industrial level can recommend how such social networks and information share can be modeled [11, 12].

Energy is required in all economic activities. The energy globally acknowledged as one of the most vital contributions that facilitate human development and growth of the economy. Increase of energy needs as a result of an increase in production output are indicators of economic growth.  The rise in energy consumption has been facilitated by technological innovation and fast population growth — usually, economic growth associated with increased energy consumption. Consequently, electrification, the rapid development of infrastructure and industrial development are indicators of economic growth.

Microeconomic as well as macroeconomic factors influence the rate at which energy is consumed. Macroeconomic factors or variables like GDP and GDP per capita indicate the income and economic activities of the population. On the other hand, microeconomic variables such as the price of energy affect energy consumption. Energy price, indicate the degree of availability of energy in the market place. Higher GDP translates to higher energy demand whereas higher energy costs lower the demand for energy.  Generally, energy consumption activities are categorized into five groups, the commercial sector, and household sector, and transportation, industrial and other areas such as agriculture. However, the target group is the household sector. The choice for the household sector due to the assumption that its sensitivity to energy is higher when compared to other industries.

In the coming years, the demand for energy will increase as a result of expansion in economic activities, economic and population growth, cost, and availability of electronic appliances. Energy consumption in households is affected by several factors demographic and physical characteristics of the home, economic as well as other factors. Energy elasticity and energy intensity are the variables used to determine the energy needs for economic growth and development. Thus, the chief objective of this research is to evaluate the factors that influence the consumption of energy in households, energy intensity and elasticity of the households in the UK.

The main focus of this research is developed a mathematical model that would enable to influence the households in a positive manner of energy saving and social network structures in the society. A case study-based analysis is conducted in this work.

Literature Review

Various research has been conducted on factors that influence energy consumption. Moreover, by tradition, other factors such as psychological characteristics and objective of residents are useful in the formulation of energy policies. For example, a positive approach towards the environment favorable to family speculation in energy efficiency. Most psychological aspects, do not affect behavior directly; instead, they influence action through intention. Intention a vital course of behavior formation and the determining factor that take place before enactment of act. Thus, the central roles of intention and behavior should be involved in the analysis of energy consumption.

The behavior of individuals is influenced by contextual factors such as the establishment of information concerning energy utilization, energy conservation guidelines, culture, price, moral values and other social norms that support energy consumption intentions. The force from the public domain has more and long-lasting effects on energy consumption when compared to price factors.

Behavior change process and measures

Behavioral change is a vital factor of study decide on energy consumption. Groundbreaking techniques and tools have been offered as a result of the increased number of projects and programs that are conducted on environmental behavior. Possibilities of energy will be reviewed by changing the behavior of people in the United Kingdom.

To advance the policy that influences household and consumer behavior environment, provide the best equipment and direction to develop and implement fruitful strategies, the Project BEHAVE has been conducted. The study conducted reveals that individual behavior depends on responsibility, motivation, and information. These factors determine the instruments that increase public awareness such as financial aspects, workshops, feedback projects, publications, and educational projects. The BEHAVE project study, the analysis of behavior change programs offer an insight into the studies carried out in many households, whereby the measures that affect consumer behavior and observation are established.

Measuring devices on energy consumption motivate people to save and acquire energy-efficient appliances. Energy consumption through behavioral change falls under two categories, primary intervention and the consequences intervention, which are applicable under particular accomplishments. Feedback is a vital tool in behavior change. Through the use of new technologies, dedicated devices can continuously track the energy consumption of household appliances. Through the use of a wide variety of innovative programs through an internet connection, household consumption trends can be monitored remotely.

The intent of households on energy conservation and consumption rely on psychological aspects more than socio-demographic. Thus, the government should not only be directed to the households, but also the suppliers. Furthermore, the government has to support advancement and development in the technological invention.

Household preferences for energy consumption and saving measures

Analysis has directed attention to the indirect as well as direct consumption of energy. Traditionally, reduction measure on the immediate waste of energy has drawn more attention when compared to the indirect standards of energy consumption. Nonetheless, more than half of the energy utilized by households consumed indirectly. Thus, minimizing indirect use of energy can attain noticeable energy savings. Indirect power can be termed as energy dedicated to the transportation, manufacturing, and sale of goods and services consumed by households. The use of less energy intensive products can significantly reduce the amount of indirect energy consumption. Approaches to a reduction in household energy consumption can be deduced to behavioral and technical change strategies. Technical strategies, usually, are costly to minimize consumption of energy and need an initial investment. The energy saving approaches can be categorized into three divisions, behavioral change, technical measures, and service consumption. The technical measures are more suitable than behavioral modification. Also, plans that minimize the direct use of energy is more acceptable when compared to reduce indirect energy consumption. Suitability of indirect energy saving methods can be maximized via policy and creation of awareness.

The expansion of the household sector in terms of energy consumption is linked to the growth of the population, use of energy per capita and increased purchasing power. They are various factors that have to be considered while analyzing energy consumption. They include lifestyle factors, economic factors, availability and accessibility to infrastructure and mindset factors. Technically, several features of households like the level of income, type of energy consumed, expenditure, household demographics, physical characteristics of the house and tools used in the home have been reviewed. They have an impact on the consumption of energy in households. Even in the prediction of household energy requirements, several factors ought to be taken into consideration; total energy consumed for cooking and lighting, the population rate of growth and number and the number of households.

While determining energy consumption, a model engineering approach is applied. In this model, technology has been taken into consideration in the energy flow processes and calculation; it is considered as a variable. This approach has made an impact in technology at the service level. When using this approach, energy demand for each activity is the outcome for two factors namely; energy services (activity level) and use of energy per unit of activity level (energy intensity). Thus, the model can be expressed as:

Whereby:

Qi= Amount of energy service, i.

Ii= Energy intensity use for energy service, i.

The quantity of energy services, Qi, is determined by various factors that comprise of population number, the ratio of total energy use, the arrangement of energy consumption, customer classification.

Generally, the growth of economic activity is closely related to an increase in energy consumption. Thus, this is regarded as linked between changes in technology and substitutions of energy. An increase in demand for energy cannot be evaded in a dynamic economic expansion, which is marked by an increased rate of production and the presence of other commercial activities. However, usage efficiency can be termed as an increase in the consumption of energy in different divisions. Energy intensity and energy elasticity are indicators to the level of energy efficiency, which mostly used. Therefore,   Energy intensity and energy elasticity impact on consumption of energy.

The increase in energy requirements needed to attain a particular economic growth rate can be termed as energy elasticity. Arithmetically, the ration of energy consumption growth rate to the economic growth rate in a state can be modeled as:

The efficiency in energy utilization in a state is inversely proportional to energy elasticity. Thus, when the energy elasticity index is below 1.00, it indicates the optimal consumption of energy.

On the other hand, the energy needed to expand Gross Domestic Product can be termed as energy intensity. Mathematically, the ratio of consumption of energy to Gross Domestic Product referred to as energy intensity. Thus, the more efficient the utilization of energy, the lower the energy intensity index. Energy intensity can be noted as a function factor of economic activity and an energy efficiency function, which is formulated as follows:

t = Time (year)

Et= total energy consumption in the year

Eit = Sector Energy Consumption in year

Yt = Gross Domestic Product

Yit = Economic Activity Measure in the sector in the year

In simple terms, the formula above state that total energy intensity is simply a function of a particular efficiency sector (Eit) and activities of a sector(Sit). Practically, high energy intensity translates to a high conversion cost to GDP, Aggregate population, the total energy per capita and the total number of households.

Consequently, in this research, based on the above theoretical review and energy data availability GDP, several households, Aggregate population, and total energy per capita influence energy consumption.

Methodology

The objective of this section is to discuss in detail the development of a mathematical model that can calculate the influence of household characteristics (income, age…) on energy saving and social network structure (complex network) on energy-saving behavior in order inform innovation in the field of residential energy saving. The model developed and the methodology adopted is presented as follows. The quantitative research methodology is used here. The quantitative research methodology helps answer questions of the phenomenon under investigation in a practical way. This answer questions like how many, percentages and statistics related to the aspect. It is useful when the objective of the research has been well established, and hypothesis-based testing is required. In this research work, the quantitative data is collected from existing research studies and is understood through various models from existing study designs.

The model has been developed keeping in mind that personal acceptance and social network influence will change individual perception and consumer behavior towards energy savings. The different acceptance levels include income, age, family situation and more. This paper focuses on the comparison of personal acceptance and social network influence which both impacts on energy efficiency product adoption.

The main problem in the analysis of such data is the quantification of real-world data to assess them better. This requires a nuanced analysis of details. For this, it is imperative to quantify the information that would be used in the process. Development of mathematical and simulation models with different levels of complexity are hence required here. To understand consumer energy-saving behavior, this has to be understood. For instance, the research work of Bale identifies how complex research models might be needed to simulate real-time conditions [13]. The models are developed such that they have parameters that can connect to the real-world factors. These can have quantified as available data. Added to the sensitivity of the model outcome to the various settings, these can be used as a guiding force. Added to the sensitivity of the model results, other parameters are used for the guidance of the effective targets for the network interventions. In the social networks identified towards energy saving, these interventions need to be implemented by the local authorities and are targeted by the domestic sector.

The model identified for this work is a weighted network model, in the form of random graphs with edges connected in probability defined by variable, between different pairs of nodes. Lack of correlations between nodes are analyzed, and so are the weight assigned to nodes. Since a household of communities is considered as part of the network, their association with one another defined through different weight, based on knowledge, or income or proximity to connections would be helpful. This research work makes use of a network designed for both E-R random networks, and W-S small world networks. Energy savings and adoption in both small and large population network is analyzed.

Mathematical Model

In a social network with  individuals which can be represented by a system with  nodes, the connections between individuals can be represented by edges. So, the connection state of the whole system can be represented by .  is the edge state from node  to node .

Assume that the network is balanced and the connection weight is the same, so . States are assigned to the nodes, describing the properties of the individuals, and deterministic or probabilistic equations or rules can be used to describe the evolution of these states over time.

Social network trend influence

It is indicated in [14] that the entire social network influences an individual’s decision making as well. This majority affect phenomenon can be understood as the individual in a social to the network has to desire to adapt to society [14]. Thus, the social influence part of the utility  includes both direct contacts from neighbour nodes and indirect influence from the whole social network.

Case Study

In the research report of Green Deal Segmentation [15], survey data are collected for the willingness of new energy efficiency product adoption. Participants’ income level, age, family situation, and working status are collected as well. To find out the relative weights of these individual, characteristics to the acceptance rate of energy efficiency product, the following assumptions are made,

All the nodes take the same value of parameters in .

The personal acceptance in the report of Green Deal is divided into six different levels with relative weights 0, 0.2, 0.4, 0.6, 0.8, one which represents rejecters, low acceptance, below average acceptance, above average acceptance, high acceptance, and accepters.

For simplification, the average value of income level, age, family situation, and working status are used for different levels of personal acceptance.

Function types of  which used to describe the relationship between acceptance levels and personal characteristics are determined through observation of survey data trends.

Calculation of  using the mean value of income, age, family situation and working status at each acceptance level.

The survey data in Green Deal Segmentation report [15] are collected and listed in the following table:

Table 6.1 Green Deal Segmentation report result by acceptance level

Personal Acceptance Coefficients

From Table 6.1 the mean value of different participants’ characteristics can be calculated according to assumptions made in section 6.2.1. After the comparison of the average value of personal characteristics with the calculated result. The trend to acceptance level with segments’ characteristics is shown in the following figures.

It can be seen from Figure 6.1 to Figure 6.4 that income level, family situation and working status all have a positive influence on the acceptance level. However, age has a negative influence on the acceptance level.

Assume that the influence of different personal characteristics is linear to the acceptance level. The equation then can be represented as,

Where , , ,  are all personal features coefficients. To calculate these coefficients least square method data fitting is applied.

Discussion in E-R Random Network

The model application in E-R random network considers a comparison of personal acceptance and social influence. Here own acceptance is understood as the personal level in which a person from the social network understands and accepts energy saving. This could differ vastly based on elements like their education, their awareness of energy saving, their understanding of how energy saving could help them, their age and earnings, etc.

In the ER random network, personal acceptance and social influence are both considered as two different motifs of analysis. Own recognition has been assigned a significant weight, and social impact is deemed to be based on connection proximity.

Some significant areas of discussion in the ER random network that has been considered concerning the model are aspects of clustering coefficient, average distance, and degree distribution [16]. Clustering coefficients in the case of the ER random networks are usually high, and hence these social network structure could be said to represent the real world well. In the real world, there would be a high amount of social living and therefore a higher number of clusters as well. Secondly, the average distance in nodes is small, which is similar to small world representations of real-world networks, and the final aspect is degree distribution. The degree distribution is considered a con here as compared to the other two aspects. Degree distributions in the network is a Poisson degree distribution, but most real-world systems will have a power-law degree distribution or a scale-free model.

In the context of looming uncertainty on the form of saving, information available on saving, the node proximity information and more, it can be argued that the Erdos-Renyi graph gives few components and diameter and hence heavy-tailed degree distributions are possible with lesser components. An important pro is that this model is well studied mathematically and hence it is considered as a model that could be significantly understood concerning the situation at hand.

Application in W-S Small-world Network

Comparison of personal acceptance and social influence concerning model in the W-S small world network could be considered as follows. The model application in the current paper can be understood to be more related to the W-S small world network than the ER random. Small world as a term was coined first by Milgram in his pioneering experiment. Watts and Strogatz later defined the analytical framework of the little world in the context of understanding social, information, technological and biological networks. The small world is considered as having many local clusters and the members themselves are connected with a very short distance or via by only a few members. Many real networks have this short world property, and although the world cannot be quantified in itself, some of the information can be quantified. Structure and dynamics can be understood as such.

Energy Savings in Small Population Network

It would be difficult to study large samples of data or even large real networks in their entirety. This is because it would be impossible to collect sufficient data on such a population. New social networks are not really scaled down to size and hence to collect complete data on such networks would not be feasible. It takes much time and resources to collect data, and even then, some amount of control in experimentation has to be introduced at this point. In such cases, studying an artificial structured network is more feasible or studying a small-scale network whose data could be useful for studying implications for large scale network is indeed better.

Energy Savings in Large Population Network

Energy savings as understood from small world networks could be emulated and understood from the significant population context, in which the number of nodes to another node are still low. It is researched and established that the number of nodes to another node is indeed tiny and hence emulating energy saving within small social structures of residential users is better than going in for larger structures.  Now modeling down with small structure network based on Green Deal income data would give implications to understand small worlds but it would also help understand similar samples of the population in other systems. Energy savings however as applied to a very large population might not account for discrepancies in geography, the income level of the individual, their awareness and more.

Implications

The work aimed to enhance the usefulness of these models incomprehension of the adoption of energy innovations. This work aimed to bridge the gaps between the actions of mathematicians in modeling homogenous networks. This is the work of the social scientists in understanding the role of the social networks in diffusions of innovations. In this research, a model was determined for exploring the parameter for space to investigate the factors that are important for the parameter space to examine the important factors. In this, the specific data requirement is for further model development parameter data for the development of the comments. These segmentations would allow for the household barrier to specific energy technologies. These are linked to the physical and economic barriers to the adoption.  The personal benefit is for the development of the economic and personal benefit for the adoption of the technology. These are considered to have cost saving and also have thermal comfort or fit with the pro-environmental lifestyle choices. The segmentation of the household weighting for personal and social influences is social norms. The emergent behavior that arises from the system indicates from the complexity-based method. The methodology has been developed further that is used for the exploration of the network interventions. These could be implemented by the local authority for the enhancement of energy technologies. In addition to this, certain relevant modifications are based on energy efficient behavior. The varieties of energy-efficient practices are used for the different properties in terms of personal preference and social influence. From this research, it was indicated that more research is needed for the comprehension of the intangible and tangible ways. The models are developed to provide the people for having useful means of drawing insights that impact the emergent behavior of a social system. The science of the networks can be used to accelerate behavior change. This is still in the infancy. Added to this, the benefits of adapting to network interventions are clear.  This is an area that requires further research.

Results and Discussions

Structure network with community-based network theory with Green Deal income data. The study on green deal highlighted the following. This was a research that was developed to the commission to understand the key groups that exist about the potential demand for the Green Deal. These are used for the motivations and the barriers. This is used to comprehend the communications channels and aide in the determination of the best way to reach them. This information is used for the aiding of the DECC develop communications and for the messages of the Green Deal.

Moreover, this is expected the value of the Green Deal Participants and in others to interest in encouraging people to aide in the energy efficiency of the homes. The segmentation was developed based on the GfK NOP conducted on a survey of 2050 owner occupier.

Along with this, the private rented tenants in Britain were considered. The interviews for the research were conducted face to face between the times of February and March 2012. Each conversation that was done was with the person responsible for deciding on the improving of the societies. It was determined that the segments had been developed amongst households that had led to the potential of the Green Deal package. These include at least one of the four energy-saving home improvements that are included in the survey. These resulted in the entire base for the segmentation. Owing to the media use and communication channel preferences, the segmentation was re-created. For this research, six segments were developed. The segment name and size were established. This was the proportion of the owner-occupiers or private rented tenants. These could benefit the one energy efficiency measure that is connected in the segment. From this, the ways of developing the messages were developed. The money-saving words include the illustrations of how energy efficiency improvements can be made through the use of the Green Deal. These would enable the houses to collect the messages for longer times. Notes about the financial elements of the Green Deal would include the availability of discounts and incentives. The use of the payment methods was used as an essential part of the incentive messages. Added to this, the words of the Green deal would lead the people to be motivated. The people were observed to require financial aspects to see the development of green energy factors. These were the factors that influence people to comprehend the ways to bring people to develop green energy requirements.

Another important aspect is the use of the demographic profile. The demography of the people also shows how people can be used to make determinations. The use of the social demography profile of the people is essential aspects, and this segmentation would lead the people to make choices about the places. The element of having children, education, social scale of the people all have been determined to be the causative factors. The motivations for demography and the requirement of the people would lead the people to be motivated into choosing the energy saving options.

Furthermore, it was determined that the messages about the pro-environmental and energy-saving aspects of the Green deal were determined to be motivating to the Carbon Savers, Convertibles and Overstretched as well. It is not possible to have the messages to be off-putting. These would lead the people to comprehend how they can develop their motivating factors. It was determined that the best way of motivating people other than the physical aspects where the intangible motivating factors. These were some of the important determinations that were made in this research paper.

The analysis of data from modeling shows the expected savings from the different network groups that are used for the promotion of-of energy efficient and renewable technologies. These are selected after the cost-benefit analyses. The models that are derived are developed from an understanding of individual behavior, and it tends to assume the rational choice. Intangible psychological motivation is observed here, and it is the limitation of the research that this approach has not been investigated in full. These two approaches are integrated into the concepts. The collation of the individual preference and the social network influences are observed in the adoption of energy innovation. The local authorities have the means to harness these influences on the advantage in encouraging increased adoption. Recent developments in complexity allow the study of the impact of the social influences on the diffusion of new theories for these impacts.

Results and Discussion

In reality, people and households have different starting points in the implementation and adoption of novelties. It is essential, thus, to put into consideration the impact of introducing a variant in the limits allocated to the various nodes within the network models. For this case, a continuous distribution or even more than two distinct thresholds can be assigned to the nodes. Data collected can be used to give information concerning the size of people with different levels of limits. However, it can be ambiguous to compute exact levels of perceived intelligence.

First and foremost, two discreet thresholds, h1 and h2 will be applied. The results of the study will be based on simulations grounded on different values for a particular selected uniform archetype as indicated in the Figure 1 below.

The model applied here is A 5 (0.1, 0.8, 0.1). There is a clear indication that the outcomes have substantial independence based on the selected values although there is a reliance on the model chosen for the study. The specific selection of the two thresholds is illustrated in figures 1 and 2. The change in behavior shows that threshold selection is vital to most of the qualitative characteristics of the simulation outcome. Some contrast of the efficiency of interferences which altered a node’s parameters would be significantly shifted subject to this choice. To investigate on the question whether the difference in parameter threshold has an impact on the entire system, a comparison is made on the case whereby each node is within the network assume equal value as the average of the two thresholds in as shown in figure 2a and 2b.

Different values of two thresholds, each assigned to half the nodes

The outcomes observed in figure 2, the threshold is homogeneously raised throughout the population to correspondingly h 5 0.35 and h 5 0.5.  It is clear that the results from Figure5 (a, b) are not similar to those of figure.  Comparison between Figure2 (b) and 3(b) reveals that there is an enormous difference in the outcomes, although the average of the population thresholds in each case is 0.5.

In Figure 3(b), the homogeneity of the parameter threshold shows that there are few new adopters. This is as a result of the very possibility for social contagion’’. In cases where there is a complex mix of thresholds with uniform average, as in Figure 2(b), the low agent thresholds can jumpstart the process by elevating some peer-average of the higher individual threshold, thus causing a higher uptake than a mid-range threshold that is uniform.  This is a contradiction to the anticipation that a maximum limit (h 5 0.9), would be moderated by a low threshold (h 5 0.1) and similarly act on the average. Therefore, this can be translated as an evolving mix of complex heterogeneous thresholds, instead of aggregated behavior of specific individual elements.

In other words, at the system level, the exact heterogeneity of the elements counts to the emergent outcome where the elements are grouped in a complex system of this nature. Moreover, further choices can be made for the values, number as well as populations of different thresholds. This is shown in Figures 4 and 5. The community is grouped into three different levels about adoption factors: high, medium and low.

Distribution, across the range.

Even though the number of specific individuals in every response made to the survey question can be estimated, there are several numbers of factors that have to be put into consideration. The survey was used to group the population into three levels. The proportion of nodes (households) allotted to each level depend on tenancy, the income of the home and the type of house. Those individuals living in rental, halls of residence or even flats are believed to be not able to adopt since they cannot change their physical fittings and fixtures. Therefore, the level of household income is associated with low, mid and high banding.

In figure 4(a), fifty percent of the population have a threshold h 5 1 who are unable to adopt. The nodes in In Figure 4(b) are minimized to the mid-range threshold. This can be one of the interventions that ought to initiate by local authorities.  For instance, for those who are on a low income should be allowed to adopt by removing barriers to adoption. Consequently, Most of the regions with the model parameter space indicate that more improved uptakes rates, despite the overall organization of the lines separating areas remain unaffected.

All thresholds from a continuous distribution can be selected instead of distinct fixed values. This is indicated in figure 5, whereby limits uniformly are chosen from 0 to 1. In this situation, all structure has vanished, and all similar models parameter values assume some standard value for the average acceptance. The make the mode more accurate, restriction on the homogeneousness of the archetypes is removed. This is achieved by dividing the number of diverse groups with distinct models, assigning a specific proportion of each node randomly.

The outcomes in Figure 6(a), with similar thresholds, illustrate the same a similar splitting between sections of success. Nevertheless, these will rely on the particular selection of models and parameters. For more actual instance of distributed thresholds, as indicated in Figure 6(b), there is a complete change in behavior. In this case, the picture is more simplified, illustrating minor sensitivity to the size of prototypes for the model. The critical response is apparent when the inaccessible subset of nodes (h 5 1) have their thresholds depressed to permit them to adopt. This show the importance of the threshold distributions applied in the model.

Summary

In the introduction section, it was mentioned about the imperative needs in the homes to reduce energy consumption. This energy consumption was wasted and was considered to be a significant factor in the increase in energy utility. This is observed to be detrimental to society as well as finances. For example, the simple aspects of sealing the windows would prevent heat from being escaped through poorly sealed windows. These inefficiencies are observed in individual houses. There are many kinds of ways in which energy is wasted. An imperative need is required to derive a solution in innovative ways to address this solution. This is based on personal preference and social influence. From the research, it was indicated that the research is required for the comprehension of tangible and intangible ways. These models in this research were developed to create a scheme for the people to derive their personal ways of reducing personal preference and social influence. It was deduced to be important in intangible and tangible ways. From the science of the process, it can be used as a motivational tool to bring in behavior change. This is still in the infancy stage. To this, the benefit of adapting to network interventions is clear. This area requires further research. It can allude from this that these are some of how a change can be enforced. However, it is imperative to comprehend that there is a much more urgent need to address these changes in the schema. This is an inception point from which the other research can be generated. The main focus of the research is to develop a mathematical model that would enable the influences of the household positively. This is used in the social structure of society. These were the deductions that were made in this research. More research is required to derive these changes in the future.

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