Tony Craig

An Approach for Assessing Clustering of Households by Electricity Usage

How a household varies their regular usage of electricity is useful information for organisations to allow accurate targeting of behaviour modification initiatives with the aim of improving the overall efficiency of the electricity network. The variability of regular activities in a household is one possible indication of that household’s willingness to accept incentives to change their behavior. An approach is presented for identifying a way of representing the variability of a household’s behaviour and developing an efficient way of clustering the households, using these measures of variability, into a few, usable groupings.To evaluate the effectiveness of the variability measures, a number of cluster validity indexes are explored with regard to how the indexes vary with the number of clusters, the number of attributes, and the quality of the attributes. The Cluster Dispersion Indicator (CDI) and the Davies-Boulden Indicator (DBI) are selected for future work developing various indicators of household behaviour variability. The approach is tested using data from 180 UK households monitored for over a year at a sampling interval of 5 minutes. Data is taken from the evening peak electricity usage period of 4pm to 8pm.

An Agent-Based Model for Simulating Environmental Behavior in an Educational Organization

Agent-based modeling (ABM) is an increasingly popular technique for modeling organizations or societies. In this paper, an ABM of environmental decisions in an academic organization is devised. The decision-making model for the agents and the social network have been constructed using data obtained by responses of individuals of the organization to a questionnaire. As the number of responses is relatively small while the number of variables measured is relatively high, and obtained decision rules should be explicit, decision trees were selected to generate the decision-making model after applying different techniques to properly preprocess the data set. Regarding the social network, two networks working in parallel were developed: the hierarchical relationships, or vertical network, and the relations of friendship and companionship, or horizontal network. After that, the effects of different policies derived from the scenarios obtained from backcasting workshops were tested, with the intention of investigating how to make policies more effective. The results obtained for the academic organization are presented.

Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day?

UK electricity market changes provide opportunities to alter households’ electricity usage patterns for the benefit of the overall electricity network. Work on clustering similar households has concentrated on daily load profiles and the variability in regular household behaviours has not been considered. Those households with most variability in regular activities may be the most receptive to incentives to change timing. Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load profile clustering. 204 UK households are analysed to find repeating patterns (motifs). Variability in the time of the motif is used as the basis for clustering households. Different clustering algorithms are assessed by the consistency of the results.

A decision-making model for environmental behavior in agent-based modeling

Agent-based modeling (ABM) is an increasingly popular technique for modeling organizations or societies. In this paper, a new approach for modeling decision-making for the environmental decisions of agents in an organization modeled using ABM is devised. The decision-making model has been constructed using data obtained by responses of individuals of the organizations to a questionnaire. As the number of responses is small, while the number of variables measured is relatively high, and obtained decision rules should be explicit, decision trees were selected to generate the model after applying different techniques to properly preprocess the data set. The results obtained for an academic organization are presented.

Shopping versus Nature? An Exploratory Study of Everyday Experiences

Although a growing volume of empirical research shows that being in nature is important for human wellbeing, the definition of what constitutes an ‘experience in nature,’ and how this is different from other types of experiences, is very often left implied. In this paper we contrast everyday experiences involving nature with a category of everyday experience in which most people regularly partake. We present an exploratory study in which people (N = 357) were explicitly asked to describe a memory they had of an everyday ‘experience which involved nature,’ as well as an everyday ‘experience which involved shopping.’ The open-ended responses to these questions were analyzed both quantitatively and qualitatively. Nature experiences were generally found to be more positive than shopping experiences, and they were more likely to be rated as ‘peaceful’ and ‘active’ compared to shopping experiences. Follow-up analyses indicate a significant interaction between experience category (nature or shopping), and the relationship between connectedness to nature and the amount of pleasure associated with that experience: The more strongly connected to nature a respondent was, the larger the disparity between the pleasantness of the shopping experience and that of the experience in nature tended to be.

An Agent-Based Prototype for Enhancing Sustainability Behavior at an Academic Environment

A prototype of an Agent-based Model (ABM) for the LOCAW (LOw Carbon At Work) projet is presented. The main goal of LOCAW is foresight to enhance behavioral and societal changes enabling the transition towards sustainable paths in Europe. It will involve examining large employer-organizations in six different countries. This paper presents a shared ontology created to ensure that maximum comparability of the case-studies is maintained.Next, a preliminary prototype, using one of the six organizations, the University of A Coruna, is shown.

Testing Scenarios to Achieve Workplace Sustainability Goals Using Backcasting and Agent-Based Modeling

Pro-environmental behaviors have been analyzed in the home, with little attention to other important contexts of everyday life, such as the workplace. The research reported here explored three categories of pro-environmental behavior (consumption of materials and energy, waste generation, and work-related commuting) in a public large-scale organization in Spain, with the aim of identifying the most effective policy options for a sustainable organization. Agent-based modeling was used to design a virtual simulation of the organization. Psychologically informed profiles of employees were defined using data gathered through a questionnaire, measuring knowledge, motivations, and ability. Future scenarios were developed using a participatory backcasting scenario development methodology, and policy tracks were derived. Dynamic simulations indicated that, to be effective, organizational policy should strengthen worker participation and autonomy, be sustained over time, and should combine different measures of medium intensity for behavior change, instead of isolated policies of high intensity.

Designing Decision Trees for Representing Sustainable Behaviours in Agents

Decisions made by workers in their daily routine have an environmental impact. The LOCAW project has analyzed the drivers and barriers for an employee to choose a particular option in large organizations. In this project, Agent-Based Models (ABM) seek to clarify interactions among relevant actors and provide insights into the necessary conditions to achieve more sustainable organizations. For theoretical and practical reasons, it was considered to use decision trees to represent the internal behavior of the agents in the model. This paper focuses on how to improve the generalization capabilities of these decision trees using feature selection and discretization techniques. The application of these techniques is intended to obtain simpler decision trees, but more accurate. Experimental results of three daily activities support the adequacy of the approach presented.

Influence of internal values and social networks for achieving sustainable organizations

The LOw Carbon At Work (LOCAW) project has studied the everyday behavior of employees in six organizations in order to achieve a more sustainable Europe. Of these six, four organizations were involved in backcasting workshops to obtain future scenarios aimed at significantly improving engagement with pro-environmental behaviors by 2050. From these scenarios policies were extracted from the workshop participants that achieve this aim in their organization. Agent Based Models (ABM) were designed to model the organizations using actual information from the organization; the design also placed special emphasis on the representation of the social network. ABMs were then used to simulate the effects of the different policies derived from the backcasting scenarios. In this paper, the results for two organizations, UDC and Aquatim, are shown. These experimental results demonstrate the influence of different social networks and internal motivations of employees to determine the effectiveness of a given policy.

Walking participants through a virtual model: how we got there and its implications

Keywords Cognition x Perception x Desktop virtual environments x Active navigation vs. passive observation Background to project In the context of urban redesign and public participation, this research aims to establish what differences are afforded between active navigation of a desktop computer model of the built environment, compared to passive observation of a walkthrough of that same model. The motivation for the research is to establish an improved method for architects and urban designers seeking to convey design ideas to their audience. Findings from the research literature on whether active navigation of desktop virtual environments offers advantages over passive observation are equivocal in many areas. For example, there are contradictory findings in relation to wayfinding, spatial orientation and scene recognition (e.g. Wilson 1999; Christou and Bulthoff 1999; Gaunet et al. 2001). However, there is greater agreement on the issue of memory for spatial layout, with researchers confirming that this is enhanced with active navigation compared to passive observation (e.g. Brooks et al. 1999; Foreman et al. 2005). Within architecture, there is evidence to suggest that the presentation of moving images (in, for example, a walkthrough) does lead to better understanding of design proposals, but at present the viewer is precisely that, a viewer of a predetermined display. In the research described here we have incorporated 3D CAD (Computer Aided Design) models into a computer game engine in order to give people the ability to actively navigate themselves through an architectural model that would previously only have been accessible to them in the form of a predetermined walkthrough. We designed a between subjects experiment to