HVAC load synchronization in smart building communities

Ghofrani, Ali; Nazemi, Seyyed Danial; Jafari, Mohsen A.

doi:10.1016/j.scs.2019.101741

Cited by 38 publications

(13 citation statements)

References 49 publications

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“…In [209], a support vector machine (SVM) was utilized to predict the building energy consumption using various inputs such as global solar radiation, temperature relative humidity and household consumption level. In [210], a feed-forward multilayer perceptron (MLP) neural network was employed to forecast the maximum and minimum points in the estimated demand profile in buildings. In [211], a deep learning algorithm was applied to predict the building energy consumption under different time resolution and time horizons.…”

Section: ) Energy Consumption and Scheduling Index Parametersmentioning

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: ) Energy Consumption and Scheduling Index Parametersmentioning

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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“…A simulated case study was also carried out applying model control and DSM strategies to a commercial building, resulting in a model to analyse this and its distinct goal of reducing peak power demand [14]. Extensive research has been conducted in the commercial sector, with other notable contributions including; the potential demand side flexibility of single function office buildings [15,16], similarly occupied buildings such as classrooms [17], smart buildings [18] and other types of air-conditioned buildings [19]. The optimised scheduling of Heating Ventilation and Air Conditioning (HVAC) systems in multi zone commercial office spaces for DR has also been studied [20], demonstrating the available opportunities to extend these concepts beyond the purpose-built and well documented commercial sector.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

2021

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Increasing participation in demand response within the industrial sector may be crucial to growing the levels of available flexible capacity required to reliably control national electricity grids as renewable generation increases to satisfy emission targets. This research aims to assist the uptake of demand response in the industrial sector by investigating risk to indoor thermal environments on industrial sites offering air handling unit capacity for demand response. This evaluation uses a systematic model-based approach, calibrated and validated with empirical data from a relevant case study industrial building to assess risk through a number of scenarios. The conditions investigated cover several relevant grid response times and durations, and national and international extreme external ambient temperatures in the past, present and future under a variety of temperature limits. The study demonstrated that there is very low risk to the case study site participating in demand response, with only 15 of 264 initial and 284 of 936 total scenarios triggering any risk. The major factors affecting risk levels identified were more stringent temperature limits and the influence of more extreme climates. The development and implementation of this concept has considerable potential to benefit industrial participants and the wider national electricity grids.

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“…Recently, researchers' attention has been drawn to optimizing temperature setpoints for cooling and heating systems. An efficient temperature setpoint control system in buildings is a practical and effective approach for managing and controlling building load [5]. In [6], the authors introduced a systematic approach for identifying the influential factors on HVAC energy consumption and quantified the savings from annual and daily setpoint selection strategies.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

2021

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Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

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HVAC load synchronization in smart building communities

Cited by 38 publications

References 49 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

Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

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