BISS 2013: Simulation for Decision Supportpszps/biss2013/resources/BISS_Lec20.pdf · 2013-07-25 ·...
Transcript of BISS 2013: Simulation for Decision Supportpszps/biss2013/resources/BISS_Lec20.pdf · 2013-07-25 ·...
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BISS 2013: Simulation for Decision Support
Lecture 20
Agent-Based Simulation Case Study
Peer-Olaf Siebers (Nottingham University)
Stephan Onggo (Lancaster University)
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Motivation
• Gain some insight into the application of agent-based simulation in a real world project
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Case Study 1 (For more details see Zhang et al 2010)
Office Building Energy Consumption
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Case Study 1: Context
• Office building energy consumption – We focus on modelling electricity consumption
– Organisational dilemma
• Need to meet the energy needs of staff
• Need to minimise its energy consumption through effective organisational energy management policies/regulations
• Objective – Test the effectiveness of different electricity management strategies,
and solve practical office electricity consumption problems
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Case Study 1: Modelling
• Electricity consumption (case study) – Base electricity consumption: security devices, information displays,
computer servers, shared printers and ventilation systems.
– Flexible electricity consumption: lights and office computers.
• Current electricity management technologies (case study) – Each room is equipped with light sensors
– Each floor is equipped with half-hourly metering system
• Strategic questions to be answered (case study) – Automated vs. manual lighting management
– Local vs. global energy consumption information
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Case Study 1: Modelling
• We distinguishing base appliances and flexible appliance – Examples for base appliances
• Security cameras
• Information displays
• Computer servers
• Refrigerators
– Examples for flexible appliances
• Lights
• Desktop computers
• Printers
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Case Study 1: Modelling
• The mathematical model – Ctotal = Cbase + Cflexible
• where Cflexible = β1*Cf1+ β2*Cf2+ … + βn*Cfn
• and Cf1 …Cfn = maximum electricity consumption of each flexible appliance
• and β1 … βn = parameters reflecting the behaviour of the electricity user
– β close to 0 = electricity user switches flexible appliances always off
– β close to 1 = electricity user leaves flexible appliances always on
– Ctotal = Cbase + (β1*Cf1+ β2*Cf2+ … + βn*Cfn)
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Case Study 1: Modelling
• Knowledge gathering – Consultations with the school's director of operations and the
university estate office
– Survey amongst the school's 200 PhD students and staff on electricity use behaviour (response rate 71.5%)
• User stereotypes – Working hour habits
• Early birds, timetable compliers, flexible workers
– Energy saving awareness
• Environment champion; energy saver; regular user; big user
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Case Study 1: Modelling
• Conceptual model
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Case Study 1: Modelling
• Energy user agent – Proactive
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Case Study 1: Modelling
• Computer agent – passive
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• Light agent – passive
• Office agent – passive
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Case Study 1: Implementation
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Case Study 1: Experimentation
• Validation – Comparing simulation and empirical results
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Automated operation: Base scenario (simulation) Automated operation: Empirical data
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Case Study 1: Experimentation
• Scenario #1 – Comparing automated and manual operation (low user interaction)
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Case Study 2 (For more details see Zhang et al 2012)
Modelling the effects of user learning on forced innovation diffusion
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Case Study 2: Context
• Modelling the effects of user learning on forced innovation diffusion – Technology adoption theories assume that users' acceptance of an
innovative technology is on a voluntary basis
– Sometimes the adoption decisions are made by a few authoritative individuals and implemented enforcedly
• Our Focus is on: – Classical consumer learning theories
– Residential energy consumer in the City of Leeds
– Interventions that local authorities can take to manage smart metering deployments
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Case Study 2: Modelling
• Residential Energy Consumer (REC) template
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Case Study 2: Modelling
• Residential Energy Consumer (REC) archetypes
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Case Study 2: Modelling
• Residential Energy Consumer (REC) agent
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Case Study 2: Implementation
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Case Study 2: Experimentation
• Validation: Simulated Load Curve vs. Real Load Curve
• Experiment: Inexperienced vs. Experiences REC
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References
• Zhang, T., Siebers, P.O. and Aickelin, U. (2010). Modelling office energy consumption: An agent based approach. In: Proceedings of the 3rd World Congress on Social Simulation (WCSS2010), 5-9 September, Kassel, Germany
• Zhang, T., Siebers, P.O. and Aickelin, U. (2012). Modelling the Effects of User Learning on Forced Innovation Diffusion. In: Proceedings of the UK OR Society Simulation Workshop 2012 (SW12), 26-28 March, Worcestershire, UK
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Questions / Comments
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