Predictive control of a building hybrid heating system for energy cost reduction

Khanmirza, Esmaeel; Esmaeilzadeh, Ahmad; Markazi, Amir H.D.

doi:10.1016/j.asoc.2016.05.005

Cited by 24 publications

(16 citation statements)

References 34 publications

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“…Buildings account for about 40% of the global energy consumption [ 1 , 2 , 3 ] and contribute to over 40% of the total world CO 2 emissions [ 4 , 5 ]. The largest contributors to this high energy consumption are heating, ventilation and air conditioning (HVAC) systems [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

Grygierek

Ferdyn-Grygierek

2018

Sensors

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An inappropriate indoor climate, mostly indoor temperature, may cause occupants’ discomfort. There are a great number of air conditioning systems that make it possible to maintain the required thermal comfort. Their installation, however, involves high investment costs and high energy demand. The study analyses the possibilities of limiting too high a temperature in residential buildings using passive cooling by means of ventilation with ambient cool air. A fuzzy logic controller whose aim is to control mechanical ventilation has been proposed and optimized. In order to optimize the controller, the modified Multiobjective Evolutionary Algorithm, based on the Strength Pareto Evolutionary Algorithm, has been adopted. The optimization algorithm has been implemented in MATLAB®, which is coupled by MLE+ with EnergyPlus for performing dynamic co-simulation between the programs. The example of a single detached building shows that the occupants’ thermal comfort in a transitional climate may improve significantly owing to mechanical ventilation controlled by the suggested fuzzy logic controller. When the system is connected to the traditional cooling system, it may further bring about a decrease in cooling demand.

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Section: Introductionmentioning

confidence: 99%

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

Grygierek

Ferdyn-Grygierek

2018

Sensors

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“…The utility, smart grid and customers often have distinct and conflicting objectives. This has motivated extensive research on multi-objective optimal resource management, notably MPC [27], [28], linear programming (LP) and non-linear programming (NLP) [4], [29], [30] as well as evolutionary algorithms (EAs) [31]- [34].…”

Section: B Demand Responsementioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and windbased systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed. INDEX TERMS Energy efficiency, home energy management systems, household appliance models, load management, optimal scheduling, smart homes, security.

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“…Brownlee and Wright (2015) used radial basis function networks to reduce the number of calls to the building energy simulation [8]. Khanmirza et al (2016) used a simplified thermal network with mechanical system controls optimized using a multi-objective genetic algorithm [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed evolutionary algorithm for co-optimization of building and district systems for early community energy masterplanning

Bucking

Dermardiros

2018

Applied Soft Computing

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Buildings play a significant role in climate change mitigation. In North America, energy used to construct and operate buildings accounts for some 40% of total energy use, largely originating from fossil fuels [1]. The strategic reduction of these energy demands requires knowledge of potential upgrades prior to a building's construction. Furthermore, renewable energy generation integrated into buildings faç ades and district systems can improve the resiliency of community infrastructure. However, loads that are non-coincidental with on-site generation can cause load balancing issues. This imbalance is due to solar resources peaking at noon, whereas building loads typically peak in the morning and late afternoon or evenings. Ideally, the combination of on-site generation and localized storage could remedy such load balancing issues while reducing the need for fossil fuels. In response to these issues, this paper contributes a methodology that co-optimizes building designs and district technologies as an integrated community energy system. A distributed evolutionary algorithm is proposed that can navigate over 10 154 potential community permutations. This is the first time in literature that a methodology demonstrates the co-optimization buildings and district energy systems to reduce energy use in buildings and balance loads at this scale. The proposed solution is reproducible and scalable for future

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Predictive control of a building hybrid heating system for energy cost reduction

Cited by 24 publications

References 34 publications

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

A Survey on Home Energy Management

Distributed evolutionary algorithm for co-optimization of building and district systems for early community energy masterplanning

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