Hourly operational assessment of HVAC systems in Mediterranean Nearly Zero-Energy Buildings: Experimental evaluation of the potential of ground cooling of ventilation air

Ascione, Fabrizio; Borrelli, Martina; Masi, Rosa Francesca De; Vanoli, Giuseppe Peter

doi:10.1016/j.renene.2020.03.180

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…In particular, the bedrooms are characterized by rapid temperature equalization, mainly in Bedroom 2, which is disadvantaged in terms of solar gains due to the prevalent exposure. As indicated in Figure 6B, the temperature distribution in Bedroom 1 determines a "slightly cold" sensation, according to PMV distribution, except at the vent where the air volume is associated with a cold flow, which corresponds (Figure 6C) to a PPD higher than the threshold value of 10% [59]. The punctual value of these indices in the room center at a height of 1.1 m (man sitting at a table) is reported in Table 7 for the coldest (Bedroom 2) and the hottest (kitchen) rooms and for the living room.…”

Section: Simulation Analysis Of Indoor Comfort Conditionsmentioning

confidence: 94%

“…The summertime investigation starts from a previous work of the authors, where it was demonstrated that the coupling of a ground-to-water heat exchanger with the two available cooling systems had great potential to achieve the nZEB target, with reference to the energy requirement for space cooling [57]. Instead, in this paper, the effect of the HVAC configurations, reported in Table 2, on indoor comfort conditions is evaluated by means of numerical simulations with EnergyPlus [58] though the DesignBuilder v.6.0 interface [59]. The numerical model of the BNZEB has been already created and calibrated in another work [48], but for the purpose of the present study, it has been upgraded, also using a new version of the software interface.…”

Section: Summertime Investigationmentioning

confidence: 99%

“…In Bedroom 1, the indices for 9 December are even better than 14 January. Instead, when the lower setpoint is set, the conditions are more critical in both rooms, but these are quite acceptable according to [51,52,57], since the PMV is 0.5. The values in the living room, once again, underline the important role of solar gains.…”

Section: Indoor Comfort Analysis By Means Of Measured Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB

et al. 2021

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This paper proposes the analysis of real monitored data for evaluating the relationship between occupants’ comfort conditions and the energy balance inside an existing, nearly zero-energy building under different operational strategies for the heating, ventilation, and air-conditioning system. During the wintertime, the adaptive comfort approach is applied for choosing the temperature setpoint when an air-to-air heat pump provides both heating and ventilation. The results indicate that in very insulated buildings with high solar gains, the setpoint should be decided taking into consideration both the solar radiation and the outdoor temperature. Indeed, when the room has large glazed surfaces, the solar radiation can also guarantee acceptable indoor conditions when a low setpoint (e.g., 18.7 °C) is considered. The electricity consumption can be reduced from 17% to 43% compared to a conventional setpoint (e.g., 20 °C). For the summertime, the analysis suggests the adoption of a dynamic approach that should be based on the outdoor conditions and differentiated according to room characteristics. Considering the indoor comfort and the maximization of renewable integration, the direct expansion system has better performance than the heat pump; this last system should be integrated with a pre-handling unit to be energy convenient.

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Section: Simulation Analysis Of Indoor Comfort Conditionsmentioning

confidence: 94%

Section: Summertime Investigationmentioning

confidence: 99%

Section: Indoor Comfort Analysis By Means Of Measured Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB

et al. 2021

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“…In the simulation, each room of the building can be divided into a thermal zone, or multiple rooms can be divided into the same thermal zone. ermal zone division is to partition the building from the perspective of building thermal engineering, rather than partitioning in the spatial sense [35,36].…”

Section: Building Energy Simulationmentioning

confidence: 99%

Digital‐Twin‐Based Evaluation of Nearly Zero‐Energy Building for Existing Buildings Based on Scan‐to‐BIM

Zhao

Hong

Wang

et al. 2021

Advances in Civil Engineering

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In recent years, the global energy environment has become increasingly severe, and the problems such as global warming, soaring carbon emissions, and excessive use of petrochemical energy have attracted increasing attention from all walks of life. The construction industry, which accounts for about 30% of the total energy consumption, needs to effectively manage and use renewable energy. The application of building information model (BIM) in the evaluation of retrofitting schemes of existing buildings is one of the main research issues in the field of building digital twins at present. The method of this research is to use 3D laser scanning technology to efficiently create the building energy model (BEM) of existing buildings and to identify and evaluate the feasibility of existing building retrofitting schemes. The purpose of this research is to evaluate the retrofitting scheme of existing buildings based on the concept of nearly zero-energy buildings (nZEBs), aiming to improve the energy efficiency of existing buildings and use clean energy to satisfy building energy demand. According to the case study in this paper, an nZEBs solution suitable for the building can reduce building energy costs by 14.1%, increase solar photovoltaic power generation by 24.13%, and reduce carbon dioxide emissions by 4306.0 kg CO2eq/a.

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“…Feng et al [21] used classical Q-learning to optimize the reward function to control the HVAC system but this is not suitable for cases with large state space. The heat transfer process was modelled in Ascione et al [22] but the authors only evaluated the solution with a single-zone building. However, in 2015 a Deep Reinforcement Learning (DRL) algorithm defeated the world champion in the game of Go.…”

Section: Research Backgroundmentioning

confidence: 99%

Scalable Smartification of Commercial Buildings HVAC Systems Using the Internet of Things and Machine Learning

Mahdi

Bakare

Ahmad

et al. 2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Most of the commercial buildings in Malaysia are still equipped with the legacy control for their Heat ventilation and air conditioning system (HVAC), which several studies claimed to have contributed to energy consumption in buildings. A significant amount of this energy is consumed by the building Heat Ventilation and Air Conditioning units. This is mostly due to the lack of smart and remote functionalities in the legacy HVAC systems to control the chillers and the Air handling units. This massive energy consumption is an antithesis to what governments all over the world are aiming for. However, scalability and deployment of low-cost resource-limited hardware embedded with control algorithms used to save energy in commercial building's Heat Ventilation and Air Conditioning (HVAC) units is a difficult engineering task. But the unprecedented advancement and perverseness of information technology services over the past two decades has led to an ever more connected world. This project will leverage the concept of the Internet of Energy to make the systems smarter and more decentralized for flexible energy usage. Modern-day devices are increasingly linked to the internet, creating what is now referred to as the internet of things (IoT). The IoT paradigm has provided technologists with the ability to remotely control devices, and with the recent progress in Machine learning (ML) and Artificial Intelligence (AI), devices are trained to make smart decisions that can independently influence human to machine interactions.

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Hourly operational assessment of HVAC systems in Mediterranean Nearly Zero-Energy Buildings: Experimental evaluation of the potential of ground cooling of ventilation air

Cited by 15 publications

References 26 publications

Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB

Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB

Digital‐Twin‐Based Evaluation of Nearly Zero‐Energy Building for Existing Buildings Based on Scan‐to‐BIM

Scalable Smartification of Commercial Buildings HVAC Systems Using the Internet of Things and Machine Learning

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