Benchmarking algorithms for resource allocation in smart buildings

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

doi:10.1109/PTC.2015.7232812

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 proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

1 Downloads (Pure)

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 language	English
Title of host publication	2015 IEEE Eindhoven PowerTech, PowerTech
Publisher	IEEE
ISBN (Electronic)	9781479976935
DOIs	https://doi.org/10.1109/PTC.2015.7232812
Publication status	Published - 31 Aug 2015
Externally published	Yes
Event	IEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands Duration: 29 Jun 2015 → 2 Jul 2015

Conference

Conference	IEEE Eindhoven PowerTech, PowerTech 2015
Country/Territory	Netherlands
City	Eindhoven
Period	29/06/15 → 2/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Particle Swarm Optimization
Genetic Algorithm
Resource allocation
Building Energy Management System

Access to Document

10.1109/PTC.2015.7232812

Cite this

@inproceedings{22dc22e7763748e6b9b87169fcd7aff8,

title = "Benchmarking algorithms for resource allocation in smart buildings",

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.",

keywords = "Particle Swarm Optimization, Genetic Algorithm, Resource allocation, Building Energy Management System",

author = "Stefanos Markidis and Elena Mocanu and Madeleine Gibescu and Nguyen, \{Phuong H.\} and Wil Kling",

year = "2015",

month = aug,

day = "31",

doi = "10.1109/PTC.2015.7232812",

language = "English",

booktitle = "2015 IEEE Eindhoven PowerTech, PowerTech",

publisher = "IEEE",

address = "United States",

note = "IEEE Eindhoven PowerTech, PowerTech 2015 ; Conference date: 29-06-2015 Through 02-07-2015",

}

TY - GEN

T1 - Benchmarking algorithms for resource allocation in smart buildings

AU - Markidis, Stefanos

AU - Mocanu, Elena

AU - Gibescu, Madeleine

AU - Nguyen, Phuong H.

AU - Kling, Wil

PY - 2015/8/31

Y1 - 2015/8/31

N2 - 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.

AB - 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.

KW - Particle Swarm Optimization

KW - Genetic Algorithm

KW - Resource allocation

KW - Building Energy Management System

UR - https://www.scopus.com/pages/publications/84951310469

U2 - 10.1109/PTC.2015.7232812

DO - 10.1109/PTC.2015.7232812

M3 - Conference contribution

AN - SCOPUS:84951310469

BT - 2015 IEEE Eindhoven PowerTech, PowerTech

PB - IEEE

T2 - IEEE Eindhoven PowerTech, PowerTech 2015

Y2 - 29 June 2015 through 2 July 2015

ER -

Benchmarking algorithms for resource allocation in smart buildings

Abstract

Conference

UN SDGs

Keywords

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