A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing

Tabares-Velasco, Paulo Cesar; Speake, Andrew; Harris, Maxwell; Newman, Alexandra M.; Vincent, Tyrone L.; Lanahan, Michael

doi:10.1016/j.apenergy.2019.01.241

Cited by 46 publications

(6 citation statements)

References 31 publications

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“…3 [68]. The thermostat is employed in BEMS with respect to minimizing power fluctuations [69]; reducing cooling electricity cost [70]; controlling space heating [71]; improving thermal comfort [72], and increasing energy efficiency [73]. This method has the simplest control operations; nevertheless, the controlled devices always operate at full or at a default capacity when they are ON, thus, resulting in a large amount of power being consumed in each operation [74].…”

Section: ) Thermostat Controlmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: ) Thermostat Controlmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…For the cases that use MLE+, the optimizations carried out are similar: Model Predictive Control (MPC) optimizations [25][26][27][28]; building energy performance improvements using phase change materials [29]; analyzing daylight conditions with Radiance [30]; or making decisions based on the thermal comfort levels [31]. It has even been used to create a black-box model with the information provided by EnergyPlus during co-simulation [32].…”

Section: • Co-simulation Between Energyplus and Matlab ®mentioning

confidence: 99%

EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#

Gordillo¹,

Ruiz

Stauffer

et al. 2020

Sustainability

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There is a growing concern about how to mitigate climate change, in which the production and use of energy has a great impact as one of the largest sources of global greenhouse gases (GHG). Buildings are responsible for a large percentage of these emissions. Therefore, there has been an increase in research in this area, in order to reduce their consumption and increase their efficiency. One of the major simulation programs used in optimization research is EnergyPlus. The purpose of this software is the complete energy simulation of a building, although it lacks tools to analyze its results and, above all, to manage and edit its simulations. For this reason, we developed an application programming interface (API) that serves to merge two areas which are highly demanded by researchers: energy building simulation (using EnergyPlus) and tools for the management and design of research experiments (in this case, MATLAB®). The developed API allows the user to perform complex simulations using EnergyPlus in a simple way, as it allows the editing of each simulation and the analysis of the simulation results through MATLAB®. In addition, it enables the user to simultaneously run multiple simulations, using either all computer core processors or a selection of them (i.e., allowing parallel computing), reducing the simulation time. The API was developed in the C# language, such that it can be used with any software that can import . N E T libraries.

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“…So far, this tool was employed in different research works to simulate the thermal behaviour of many types of buildings [37,38].…”

Section: Building Descriptionmentioning

confidence: 99%

Economic evaluation of a hybrid heating system in different climate zones based on model predictive control

Tarragona

Fernàndez

Cabeza

et al. 2020

Energy Conversion and Management

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A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing

Cited by 46 publications

References 31 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#

Economic evaluation of a hybrid heating system in different climate zones based on model predictive control

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