Benchmarking algorithms for resource allocation in smart buildings

Stefanos Markidis, Elena Mocanu, Madeleine Gibescu, Phuong H. Nguyen, Wil Kling

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

The energy allocation at the building level is a complex decision making process. To cope with the uncertainties introduced by the user behavior, new energy-intensive technologies, and renewable energy sources, a real-time adaptation of the building energy management system is required. This paper presents a benchmark of energy resource optimization system for smart buildings, and examines different solution approaches, such as MiniMax Algorithm (MM), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (Q-PSO). These mathematical and heuristic optimization techniques are all able to find the optimal tradeoff between various resources and demands in the system. The proposed method and solution algorithms were tested on a simulated office building, which is powered by two sources of energy, one conventional, and one renewable, i.e. rooftop photovoltaics.

Original languageEnglish
Title of host publication2015 IEEE Eindhoven PowerTech, PowerTech
PublisherIEEE
ISBN (Electronic)9781479976935
DOIs
Publication statusPublished - 31 Aug 2015
Externally publishedYes
EventIEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

ConferenceIEEE Eindhoven PowerTech, PowerTech 2015
CountryNetherlands
CityEindhoven
Period29/06/152/07/15

Keywords

  • Particle Swarm Optimization
  • Genetic Algorithm
  • Resource allocation
  • Building Energy Management System

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