A state-of-the-art review on shallow geothermal ventilation systems with thermal performance enhancement-system classifications,advanced technologies and applications

Geothermal energy with abundance and large quantity can partially cover building heating/cooling loads and promote the carbon-neutrality transitions.Shallow geothermal ventilation(SGV)system,with a little initial in-vestment cost,is one of promising technologies to partly replace the conventional air-conditioning system for air pre-cooling/pre-heating.This paper reviews applications of SGV system for improving thermal performance over latest two decades,which mainly includes the reclassification of SGV system,coupling with other advanced energy-saving technologies,application potentials for building cooling/heating under various weather conditions.Heat transfer mechanism and mathematical modelling techniques have been reviewed,together with in-depth analysis on current research trends,existing limitations,and recommendations of SGV system.Phase change materials,with considerable latent energy density,can stabilize the thermal performance with high reliability.The review identifies that optimization designs and advanced approaches need to be investigated to address the existing urgent issues of SGV system(e.g.,large land occupation,difficulty in centralized collection of condensate water timely for horizontal buried pipe,bacteria growth,polluted supply air,and high construction cost for ver-tical buried pipe).A plenty of studies show that the SGV system could greatly expand the application scope and improve system energy efficiency by combining with other energy-saving technologies.This paper will provide some guidelines for the scientific researchers and engineers to keep track on recent advancements and research trends of SGV system for the building thermal performance enhancement and pave path for future research works.

Evaluation tool for the thermal performance of retrofitted buildings using an integrated approach of deep learning artificial neural networks and infrared thermography

In most countries,buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use.To reduce this massive consumption and decrease carbon emission,thermal insulation of buildings can play an important role.The estimation of energy savings following the improvement of a building’s insulation remains a key area of research in order to calculate the cost savings and the payback period.In this paper,a case study has been presented where deep retrofitting has been introduced to an existing building to bring it closer to a Passivhaus standard with the introduction of insulation and solar photovoltaic panels.The thermal performance of the building with its improved insulation has been evaluated using infrared thermography.Artificial intelligence using deep learning neural networks is implemented to predict the thermal performance of the building and the expected energy savings.The prediction of neural networks is compared with the actual savings calculated using historical weather data.The results of the neural network show high accuracy of predicting the actual energy savings with success rate of about 82%when compared with the calculated values.The results show that this suggested approach can be used to rapidly predict energy savings from retrofitting of buildings with reasonable accuracy,hence providing a practical rapid tool for the building industry and communities to estimate energy savings.A mathematical model has been also developed which has indicated a life-long monitoring will be needed to precisely estimate the benefits of energy savings in retrofitting due to the change in weather conditions and people’s behaviour.

Development of a short duration method to assess the envelope thermal performance of multi-family housings

Building energy efficiency is a key factor in reducing CO_(2) emissions.For this reason,European Union(EU)member states have developed thermal regulations to ensure building thermal performance.These results are often based on results achieved with building simulation software during the design stage.However,the actual thermal performance can deviate significantly from the predicted one,and this difference is known as the energy performance gap.Accurate indicators of the actual thermal performance are a valuable tool to guarantee building quality.These indicators,including the heat transfer coefficient(HTC)and the heat loss coefficient(HLC),can be estimated by the application of in situ methods.As multi-family housing and tertiary sector buildings are an important part of the building stock,mature methods to measure their thermal performance are needed.This paper presents a short-duration method for assessing the HTC in large building typologies using a sampling approach.The method was applied in a four-storey building model under different conditions to study the limits of the method and to improve indicator bias and uncertainty.Indicator quality was strongly influenced by the external weather conditions,the temperature variation during the protocol and the heat exchange with the adjacent apartments.Under winter conditions and with stable indoor temperatures,the method had a high accuracy when the protocol was applied for half a day.It is recommended that the protocol be used over two days to improve indicator quality under less favorable test conditions.

Method for determining climatic design conditions based on the indoor thermal environment risk level

Currently,climatic design conditions are usually selected according to the frequency of climatic parameters them-selves,which method cannot reflect the indoor thermal environment risk level of the building in design.In this regard,the research proposes to construct the correlation between climatic design conditions and indoor thermal environment risk level,and explore the effect of uncertainty in building thermal performance on this correlation from the perspective of probability,thus realizing the process of selecting the climatic design conditions based on the requirement for indoor thermal environment risk level.Taking Guangzhou in China as an example,the new process of determining climatic design conditions is realized.On this basis,the difference between the traditional method and the present research method is compared.In the Chinese norm method,the indoor thermal environ-ment risk level of the building is between 0 and 0.03%when the climatic design conditions are selected with 0.57%cumulative frequency of occurrence;in the research method,the indoor thermal environment risk level of the building is between 0.2%and 0.6%when the climatic design conditions are selected with 0.57%indoor thermal environment risk level and 100%confidence level.The results indicate that the research method can meet the designer’s expectation for indoor thermal environment risk level in design more directly and accurately.