Agent-based models for residential energy consumption and intervention simulation

The increase in energy consumption in buildings has gained global concern due to its negative implications on the environment. A major part of this increase is attributed to human behavioural energy waste, which has triggered the development of energy simulation models. These models are used to analyse energy consumption in buildings, study the effect of human behaviour and test the effectiveness of energy interventions. However, existing models are limited in simulating realistic and detailed human dynamics, including occupant interaction with appliances, with each other or with energy interventions. This detailed interaction is important when simulating and studying behavioural energy waste. To overcome the limitations of existing models, this thesis proposes a complete layered Agent-Based Model (ABM) composed of three layers / models. The daily behaviour model simulates realistic and detailed behaviour of occupants by integrating a Probabilistic Model (PM) in the ABM. The peer pressure model simulates family-level peer pressure effect on the energy consumption of the house. This model is underpinned using well established human behaviour theories by Leon Festinger – informal social communication theory, social comparison theory and cognitive dissonance theory. The messaging intervention model implements and tests a novel messaging intervention that is proposed in the thesis along with the complete ABM. The intervention is a middle solution between the abstract data presented by existing energy feedback systems and the automated approach followed by existing energy management systems. Therefore, it detects and sends energy waste incidents to occupants who are allowed to take control of their devices. The proposed intervention is tested in the messaging intervention model, which takes advantage of the two other proposed models. The undertaken experiments showed that the model is able to overcome the limitations of exiting models by simulating realistic and detailed human behaviour dynamics. Besides, the experiments showed that the model can be used by policy makers to decide how to target family members to achieve optimal energy saving, thus addressing the world’s concern about increased energy consumption levels

A Non-intrusive Heuristic for Energy Messaging Intervention Modelled using a Novel Agent-based Approach

In response to the increased energy consumption in residential buildings, various efforts have been devoted to increase occupant awareness using energy feedback systems. However, it was shown that feedback provided by these systems is not enough to inform occupant actions to reduce energy consumption. Another approach is to control energy consumption using automated energy management systems. The automatic control of appliances takes-out the occupant sense of control, which is proved to be uncomfortable in many cases. This paper proposes an energy messaging intervention that keeps the control for occupants whilst supporting them with actionable messages. The messages inform occupants about energy waste incidents happening in their house in real-time, which enables occupants to take actions to reduce their consumption. Besides, a heuristic is defined to make the intervention non-intrusive by controlling the rate and time of the messages sent to occupants. The proposed intervention is evaluated in a novel layered agentbased model. The first layer of the model generates detailed energy consumption and realistic occupant activities. The second layer is designed to simulate the peer pressure effect on the energy consumption behaviour of the individuals. The third layer is a customisable layer that simulates energy interventions. The implemented intervention in this paper is the proposed non-intrusive messaging intervention. A number of scenarios are presented in the experiments to show how the model can be used to evaluate the proposed intervention and achieve energy efficiency targets

An Agent-based Collective Model to Simulate Peer Pressure Effect on Energy Consumption

This paper presents a novel model for simulating peer pressure effect on energy awareness and consumption of families. The model is built on two well-established theories of human behaviour to obtain realistic peer effect: the collective behaviour theory and the theory of cognitive dissonance. These theories are implemented in a collective agentbased model that produces fine-grained behaviour and consumption data based on social parameters. The model enables the application of different energy efficiency interventions which aim to obtain more aware occupants and achieve more energy saving. The presented experiments show that the implemented model reflects the human behaviour theories. They also provide examples of how the model can be used as an analytical tool to interpret the effect of energy interventions in the given social parameters and decide the optimal intervention needed in different cases

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Modeling and simulation of multi-cellular systems using hybrid FEM/Agent-based approaches

Muchas de las propiedades biomecánicas de los organismos multicelulares surgen directamente de las interacciones entre células. Las células de los órganos y tejidos interactúan entre sí y con su entorno de diferentes formas. Debido a este hecho, es fundamental analizar cómo estas interacciones se traducen como propiedades mecánicas a nivel del tejido. Por ejemplo, las adhesiones entre células determinan la rigidez aparente de una capa epitelial. Las interacciones célula-matriz pueden además determinar la formación de muchas estructuras biológicas y su morfología. Estos sistemas multicelulares no se pueden considerar como estructuras estáticas ya que sufren constantes cambios causados por la proliferación, la reorganización o la migración celular. Por lo tanto, es necesario estudiar la dinámica de la célula y las interacciones individuales para comprender plenamente cómo funcionan los fenómenos a escalas superiores, desde el desarrollo de tejidos hasta el crecimiento de tumores.Recientemente, el uso de enfoques basados en agentes se ha vuelto muy popular para modelar sistemas multicelulares. Los modelos basados en agentes representan células como entidades individuales. Estos modelos son especialmente adecuados para estudiar fenómenos biofísicos que ocurren a nivel celular. Aquí las interacciones célula-célula se pueden simular directamente de forma mecanicista. Además, estos modelos capturan realmente bien las heterogeneidades presentes en las estructuras biológicas. Por otra parte, los modelos continuos se utilizan comúnmente en problemas de escalas mayores. A diferencia de los modelos basados en agentes, en estos no representan células como entidades individuales, sino que se definen leyes constitutivas para modelar procesos biológicos, físicos y químicos. Por lo tanto, las propiedades celulares se promedian usando parámetros macroscópicos, y estos modelos a menudo trabajan con la densidad celular en lugar de entidades celulares separadas. En cualquier caso, los modelos continuos presentan una buena escalabilidad y una excelente representación de fenómenos físicos particulares como el transporte masivo y las transmisiones de fuerza en medios continuos.En esta tesis, se exploran las posibilidades que los enfoques híbridos pueden ofrecer para desarrollar nuevos modelos de sistemas multicelulares. Se presentan dos modelos híbridos diferentes que combinan un modelo basado en agentes y un modelo continuo. Ambos enfoques tienen en común que el modelo continuo se resuelve utilizando el método de los elementos finitos. También se muestra, siguiendo este patrón de diseño, cómo resolver varias de las limitaciones intrínsecas de cada tipo individual de modelo.En primer lugar, se presenta un modelo híbrido para simular la mecánica epitelial monocapa. Este modelo se centra en el modelado de las interacciones mecánicas célula-célula y célula-sustrato, pero también en la topología y morfología de los tejidos. Con este enfoque se reproducen tejidos epiteliales proliferativos, movimientos celular colectivo y procesos de migración. El segundo modelo presentado en esta tesis se ha diseñado para simular agregados celulares en entornos tridimensionales. Se estudian las interacciones mecánicas entre células, pero este modelo se centra especialmente en analizar cómo afecta el transporte de oxígeno a las células en un proceso de agrupamiento en 3D.Finalmente, se comparan los resultados de ambos modelos con datos experimentales de otros autores y se discuten los beneficios de combinar diferentes tipos de modelos. Se demuestra que los enfoques híbridos que se proponen en este trabajo son capaces de simular una amplia variedad de sistemas multicelulares. De hecho, son particularmente útiles para estudiar cómo algunos fenómenos emergen de las interacciones celulares individuales a escalas biológicas más grandes.<br /

Prosumer behaviour in emerging electricity systems

This dissertation investigates the interface between technology and society in the emerging electricity systems and in particular the role of the energy prosumer in the energy transition. It contributes to the understanding of the role of consumers in emerging electricity systems within the current EU energy policy context where consumer active participation is regarded as "a prerequisite for managing the energy transition successfully and in a cost-effective way". Emerging energy systems are characterized by a high level of complexity, especially for what concerns the behaviour of social actors. Social actors interact through physical and social networks by sharing information and learning from one another through social interactions. These interactions determine self-organization and emergent behaviours in energy consumption patterns and practices. I argue that the best suited tool to study emergent behaviours in energy consumption patterns and practices, and to investigate how consumers' preferences and choices lead to macro behaviours is agent based modelling. To build a sound characterization of the energy prosumer, I review the current social psychology and behavioural theories on sustainable consumption and collect evidence from EU energy prosumers surveys, studies and demand side management pilot projects. I employ these findings to inform the development of an agent based model of the electricity prosumer, Subjective Individual Model of Prosumer – SIMP, and its extended version, SIMP-N, that includes the modelling of the social network. I apply SIMP and SIMP-N models to study the emergence in consumer systems and how values and beliefs at consumer level (as defined by social psychology and behavioural theories and informed by empirical evidence) and social dynamics lead to macro behaviours. More specifically, I explore the diffusion of smart grid technologies enabled services among a population of interacting prosumers and evaluate the impact of such diffusion on individual and societal performance indicators under different policy scenarios and contextual factors. The analysis of the simulation results provides interesting insights on how different psychological characteristics, social dynamics and technological elements can strongly influence consumers' choices and overall system performance. I conclude proposing a framework for an integrated approach to modelling emerging energy systems and markets that extend the SIMP model to also include markets, distribution system operator and the electricity network