Pub Date : 2023-02-08DOI: 10.1080/17512549.2023.2174186
Mahsa Ghasemi, F. Haghighat, Chang-Seo Lee, M. Namdari
ABSTRACT Accurate measurement of ozone concentration, especially in workplaces is a crucial component of managing indoor air quality and protecting workers’ and building occupants’ health and safety. Some factors such as gaseous pollutants (like volatile organic compounds (VOCs)), relative humidity, and air velocity and direction could interfere with monitor readings. This study examined the impact of these environmental factors on the responses of six commercial ozone monitors: three UV photometry, two electrochemical and one semiconductor metal oxide. The results demonstrated that environmental physical parameters (i.e. air velocity and relative humidity) often slightly affected UV instrument’s performance, while significant effects were seen in electrochemical and semiconductor monitors. Furthermore, chemical parameters (only VOCs including ethanol, acetone and toluene) had more influence on UV ozone monitors than those using electrochemical and metal oxide techniques.
{"title":"The effect of VOC and environmental parameters on ozone sensors performance","authors":"Mahsa Ghasemi, F. Haghighat, Chang-Seo Lee, M. Namdari","doi":"10.1080/17512549.2023.2174186","DOIUrl":"https://doi.org/10.1080/17512549.2023.2174186","url":null,"abstract":"ABSTRACT Accurate measurement of ozone concentration, especially in workplaces is a crucial component of managing indoor air quality and protecting workers’ and building occupants’ health and safety. Some factors such as gaseous pollutants (like volatile organic compounds (VOCs)), relative humidity, and air velocity and direction could interfere with monitor readings. This study examined the impact of these environmental factors on the responses of six commercial ozone monitors: three UV photometry, two electrochemical and one semiconductor metal oxide. The results demonstrated that environmental physical parameters (i.e. air velocity and relative humidity) often slightly affected UV instrument’s performance, while significant effects were seen in electrochemical and semiconductor monitors. Furthermore, chemical parameters (only VOCs including ethanol, acetone and toluene) had more influence on UV ozone monitors than those using electrochemical and metal oxide techniques.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"172 - 192"},"PeriodicalIF":2.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47576487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/17512549.2023.2171486
Poorya Mehrzad, H. Taghaddos, Ala Nekouvaght Tak
ABSTRACT Building materials, mainly steel and concrete used in residential buildings, significantly impact CO2-eq emissions. Nowadays, carbon tax policy is used in some countries to reduce carbon emission effects. However, few comprehensive studies have been conducted to investigate the overall impact of such a carbon tax policy on minimizing building CO2-eq emissions. Stakeholders in the building industry often focus on cost criteria to select construction materials, neglecting environmental factors. Providing a cost-emission framework can be beneficial in choosing appropriate materials based on financial and environmental factors. This study proposes a comprehensive framework to analyze the embodied CO2 equivalent emission, carbon taxation, and cost of steel and Reinforced Concrete (RC) structures employing Life Cycle Assessment (LCA) methodology. The framework is implemented on an actual residential building case study to validate its potency. The case study’s results show that the embodied CO2-eq emission of the RC structure is 36% higher than the emission in a similar steel structure leading to 36% more carbon taxation. However, the total material cost of the steel structure is around 65% higher than the RC structure. Thus, carbon taxation policy does not necessarily reduce embodied CO2-eq emissions because stakeholders may prioritize the cost criteria to select building materials.
{"title":"Environmental-cost framework to investigate impacts of carbon tax policy on material selection for building structures","authors":"Poorya Mehrzad, H. Taghaddos, Ala Nekouvaght Tak","doi":"10.1080/17512549.2023.2171486","DOIUrl":"https://doi.org/10.1080/17512549.2023.2171486","url":null,"abstract":"ABSTRACT Building materials, mainly steel and concrete used in residential buildings, significantly impact CO2-eq emissions. Nowadays, carbon tax policy is used in some countries to reduce carbon emission effects. However, few comprehensive studies have been conducted to investigate the overall impact of such a carbon tax policy on minimizing building CO2-eq emissions. Stakeholders in the building industry often focus on cost criteria to select construction materials, neglecting environmental factors. Providing a cost-emission framework can be beneficial in choosing appropriate materials based on financial and environmental factors. This study proposes a comprehensive framework to analyze the embodied CO2 equivalent emission, carbon taxation, and cost of steel and Reinforced Concrete (RC) structures employing Life Cycle Assessment (LCA) methodology. The framework is implemented on an actual residential building case study to validate its potency. The case study’s results show that the embodied CO2-eq emission of the RC structure is 36% higher than the emission in a similar steel structure leading to 36% more carbon taxation. However, the total material cost of the steel structure is around 65% higher than the RC structure. Thus, carbon taxation policy does not necessarily reduce embodied CO2-eq emissions because stakeholders may prioritize the cost criteria to select building materials.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"98 - 124"},"PeriodicalIF":2.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45611929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-24DOI: 10.1080/17512549.2022.2160812
Bin Ran, Shuting Qiu, Ying Zhang, Li-dong Zeng, Jianming Zhu, Zhifeng Xiang, Jibo Long
ABSTRACT The energy consumption of different air conditioning systems varies greatly. In order to analyse the energy consumption status and energy-saving potential of air conditioning in existing office buildings, this paper takes an existing office building in hot summer and cold winter area as an example. The office and air conditioning energy consumption were continuously measured. The air conditioning energy consumption analysis model was established. With the measured data as input, the simulation calculation of the annual air conditioning energy consumption was carried out. The results showed that the measured value was quite different from the recommended value of the building energy efficiency standard. Changes in indoor air temperature or fresh air volume had a significant impact on air conditioning energy consumption. When the indoor set air conditioning temperature was 22°C in summer and 22°C in winter, the fresh air volume increased from 0 to 180 m3/(p·h), the increase of air conditioning power consumption reached 109.9% and 115.2%, respectively. The energy-saving envelope of the office building can obtain about 15% of the energy-saving benefits. However, users’ adjustment of operating parameters such as fresh air volume and indoor design temperature can easily lead to energy loss greater than that of energy-saving envelope.
{"title":"Air conditioning energy consumption measurement and saving strategy analysis for an office building in hot summer and cold winter area","authors":"Bin Ran, Shuting Qiu, Ying Zhang, Li-dong Zeng, Jianming Zhu, Zhifeng Xiang, Jibo Long","doi":"10.1080/17512549.2022.2160812","DOIUrl":"https://doi.org/10.1080/17512549.2022.2160812","url":null,"abstract":"ABSTRACT The energy consumption of different air conditioning systems varies greatly. In order to analyse the energy consumption status and energy-saving potential of air conditioning in existing office buildings, this paper takes an existing office building in hot summer and cold winter area as an example. The office and air conditioning energy consumption were continuously measured. The air conditioning energy consumption analysis model was established. With the measured data as input, the simulation calculation of the annual air conditioning energy consumption was carried out. The results showed that the measured value was quite different from the recommended value of the building energy efficiency standard. Changes in indoor air temperature or fresh air volume had a significant impact on air conditioning energy consumption. When the indoor set air conditioning temperature was 22°C in summer and 22°C in winter, the fresh air volume increased from 0 to 180 m3/(p·h), the increase of air conditioning power consumption reached 109.9% and 115.2%, respectively. The energy-saving envelope of the office building can obtain about 15% of the energy-saving benefits. However, users’ adjustment of operating parameters such as fresh air volume and indoor design temperature can easily lead to energy loss greater than that of energy-saving envelope.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"73 - 97"},"PeriodicalIF":2.0,"publicationDate":"2022-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44054581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-05DOI: 10.1080/17512549.2022.2152865
Seyed Shayan Shahrestani, Z. Zomorodian, Maryam Karami, Fatemeh Mostafavi
ABSTRACT Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.
{"title":"A novel machine learning-based framework for mapping outdoor thermal comfort","authors":"Seyed Shayan Shahrestani, Z. Zomorodian, Maryam Karami, Fatemeh Mostafavi","doi":"10.1080/17512549.2022.2152865","DOIUrl":"https://doi.org/10.1080/17512549.2022.2152865","url":null,"abstract":"ABSTRACT Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"53 - 72"},"PeriodicalIF":2.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42129961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-17DOI: 10.1080/17512549.2022.2146188
Mingju Gong, Jiawang Sun, Yin Zhao, C. Han, Bo Yan, Guannan Sun
ABSTRACT Accurate heat load prediction is a prerequisite for feed-forward control and on-demand heat supply in district heating system. However, considering that the experimental data used to train the prediction model are often not optimal or most energy efficient, accurate prediction is difficult to achieve effective energy-saving. This paper proposes a hybrid energy-saving prediction model that combines similar sample selection approach (SSA) and deep neural network. A new weighted Euclidean norm (WEN) is used to select suitable similar sample datasets, and a novel energy-saving strategy is proposed to reduce energy consumption. To make the prediction performance more stable, a low-pass filter is used to filter the prediction results. In the case study, real data from a heat exchange station in Tianjin are used to verify the prediction performance of the hybrid model for 1 test day, 3 test days, and 7 test days. Experimental results show that: (a) the proposed model is able to capture the change trend of heat load, with Pearson correlation coefficient of 0.971, 0.969, and 0.954 on different test days, respectively; (b) the proposed model is able to effectively reduce energy consumption, with energy-saving of 5.4%, 7.6%, and 4.8% on different test days, respectively.
{"title":"A hybrid energy-saving prediction model based on SSA-DNN for district heating system","authors":"Mingju Gong, Jiawang Sun, Yin Zhao, C. Han, Bo Yan, Guannan Sun","doi":"10.1080/17512549.2022.2146188","DOIUrl":"https://doi.org/10.1080/17512549.2022.2146188","url":null,"abstract":"ABSTRACT Accurate heat load prediction is a prerequisite for feed-forward control and on-demand heat supply in district heating system. However, considering that the experimental data used to train the prediction model are often not optimal or most energy efficient, accurate prediction is difficult to achieve effective energy-saving. This paper proposes a hybrid energy-saving prediction model that combines similar sample selection approach (SSA) and deep neural network. A new weighted Euclidean norm (WEN) is used to select suitable similar sample datasets, and a novel energy-saving strategy is proposed to reduce energy consumption. To make the prediction performance more stable, a low-pass filter is used to filter the prediction results. In the case study, real data from a heat exchange station in Tianjin are used to verify the prediction performance of the hybrid model for 1 test day, 3 test days, and 7 test days. Experimental results show that: (a) the proposed model is able to capture the change trend of heat load, with Pearson correlation coefficient of 0.971, 0.969, and 0.954 on different test days, respectively; (b) the proposed model is able to effectively reduce energy consumption, with energy-saving of 5.4%, 7.6%, and 4.8% on different test days, respectively.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"30 - 52"},"PeriodicalIF":2.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41582898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-26DOI: 10.1080/17512549.2022.2136240
H. Hosamo, M. Hosamo, Henrik Nielsen, P. R. Svennevig, K. Svidt
ABSTRACT This study proposes a novel Digital Twin framework of heating, ventilation, and air conditioning (HVACDT) system to reduce energy consumption while increasing thermal comfort. The framework is developed to help the facility managers better understand the building operation to enhance the HVAC system function. The Digital Twin framework is based on Building Information Modelling (BIM) combined with a newly created plug-in to receive real-time sensor data as well as thermal comfort and optimization process through Matlab programming. In order to determine if the suggested framework is practical, data were collected from a Norwegian office building between August 2019 and October 2021 and used to test the framework. An artificial neural network (ANN) in a Simulink model and a multiobjective genetic algorithm (MOGA) are then used to improve the HVAC system. The HVAC system is comprised of air distributors, cooling units, heating units, pressure regulators, valves, air gates, and fans, among other components. In this context, several characteristics, such as temperatures, pressure, airflow, cooling and heating operation control, and other factors are considered as decision variables. In order to determine objective functions, the predicted percentage of dissatisfied (PPD) and the HVAC energy usage are both calculated. As a result, ANN's decision variables and objective function correlated well. Furthermore, MOGA presents different design factors that can be used to obtain the best possible solution in terms of thermal comfort and energy usage. The results show that the average cooling energy savings for four days in summer is roughly 13.2%, and 10.8% for the three summer months (June, July, and August), keeping the PPD under 10%. Finally, compared to traditional approaches, the HVACDT framework displays a higher level of automation in terms of data management.
{"title":"Digital Twin of HVAC system (HVACDT) for multiobjective optimization of energy consumption and thermal comfort based on BIM framework with ANN-MOGA","authors":"H. Hosamo, M. Hosamo, Henrik Nielsen, P. R. Svennevig, K. Svidt","doi":"10.1080/17512549.2022.2136240","DOIUrl":"https://doi.org/10.1080/17512549.2022.2136240","url":null,"abstract":"ABSTRACT This study proposes a novel Digital Twin framework of heating, ventilation, and air conditioning (HVACDT) system to reduce energy consumption while increasing thermal comfort. The framework is developed to help the facility managers better understand the building operation to enhance the HVAC system function. The Digital Twin framework is based on Building Information Modelling (BIM) combined with a newly created plug-in to receive real-time sensor data as well as thermal comfort and optimization process through Matlab programming. In order to determine if the suggested framework is practical, data were collected from a Norwegian office building between August 2019 and October 2021 and used to test the framework. An artificial neural network (ANN) in a Simulink model and a multiobjective genetic algorithm (MOGA) are then used to improve the HVAC system. The HVAC system is comprised of air distributors, cooling units, heating units, pressure regulators, valves, air gates, and fans, among other components. In this context, several characteristics, such as temperatures, pressure, airflow, cooling and heating operation control, and other factors are considered as decision variables. In order to determine objective functions, the predicted percentage of dissatisfied (PPD) and the HVAC energy usage are both calculated. As a result, ANN's decision variables and objective function correlated well. Furthermore, MOGA presents different design factors that can be used to obtain the best possible solution in terms of thermal comfort and energy usage. The results show that the average cooling energy savings for four days in summer is roughly 13.2%, and 10.8% for the three summer months (June, July, and August), keeping the PPD under 10%. Finally, compared to traditional approaches, the HVACDT framework displays a higher level of automation in terms of data management.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"125 - 171"},"PeriodicalIF":2.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45635390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-03DOI: 10.1080/17512549.2022.2129449
Chittella Ravichandran, G. Padmanaban
ABSTRACT India's energy security scenario (IESS 2047) projects a mammoth increase in residential air conditioners, from 21.8 million units in 2017 to 154.4 million in 2038. This increased demand for space cooling accounts for an equal responsibility from architects and energy engineers to research building design-related cooling load nexus. This paper studies the pattern of cooling load dispersion between floors for 25 dominant residential typologies in Navi Mumbai that vary in heights, shapes, footprint areas, and densities. Simulation for cooling loads is done using Rhinoceros 6 tool with energy plus plugin. Tukey Honest Significant Difference (HSD) Post Hoc Test is done after ANOVA to group floors with similar cooling load profiles. The results show a strict increase in cooling load till the top floor for low rise and mid-rise. However, for high-rise buildings, most intermediate floors fall under a single subset category; thereby, the increase in cooling load among floors is not similar. This shows that as building height increases, the difference between cooling loads of intermediate floors decreases significantly. Also, an increase in height with a decrease in footprint area reduces the overall cooling load of the building.
{"title":"A numerical simulation-based method to predict floor wise distribution of cooling loads in Indian residences using Tukey honest significant difference test","authors":"Chittella Ravichandran, G. Padmanaban","doi":"10.1080/17512549.2022.2129449","DOIUrl":"https://doi.org/10.1080/17512549.2022.2129449","url":null,"abstract":"ABSTRACT India's energy security scenario (IESS 2047) projects a mammoth increase in residential air conditioners, from 21.8 million units in 2017 to 154.4 million in 2038. This increased demand for space cooling accounts for an equal responsibility from architects and energy engineers to research building design-related cooling load nexus. This paper studies the pattern of cooling load dispersion between floors for 25 dominant residential typologies in Navi Mumbai that vary in heights, shapes, footprint areas, and densities. Simulation for cooling loads is done using Rhinoceros 6 tool with energy plus plugin. Tukey Honest Significant Difference (HSD) Post Hoc Test is done after ANOVA to group floors with similar cooling load profiles. The results show a strict increase in cooling load till the top floor for low rise and mid-rise. However, for high-rise buildings, most intermediate floors fall under a single subset category; thereby, the increase in cooling load among floors is not similar. This shows that as building height increases, the difference between cooling loads of intermediate floors decreases significantly. Also, an increase in height with a decrease in footprint area reduces the overall cooling load of the building.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"17 1","pages":"1 - 29"},"PeriodicalIF":2.0,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48719789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-03DOI: 10.1080/17512549.2022.2067229
Jingyu Yuan, Yuting Cong, Sheng Yao, Chenrui Dai, Yitong Li
ABSTRACT At present, the thermal comfort evaluation index has not been differentiated for different types of people, and the indoor thermal environment of the residences in rural areas of cold climate, China, urgently need attention. In this study, indoor living environment of rural residences in Hebei and Tianjin were measured to obtain typical residential forms, building thermal parameters and thermal comfort parameters of the elderly. At the same time, 152 elderly people were investigated by questionnaire to obtain the subjective evaluation such as thermal sensation. In addition, the optimization suggestions of plane structure, building envelope and window to wall ratio were put forward through software simulation. The results show that the thermal neutral temperature of the rural elderly people in cold zone is 16.74°C, and they are more sensitive to the temperature below 15.67°C and more slowly to the temperature above 15.67°C. The recommended thickness of wall insulation layer is 80–100 mm, and that of roof insulation layer is 60–80 mm. Double layer glass should be used. Wood window frame has better thermal insulation effect than aluminium frame, and the recommended window to wall ratio is 0.28.
{"title":"Research on the thermal comfort of the elderly in rural areas of cold climate, China","authors":"Jingyu Yuan, Yuting Cong, Sheng Yao, Chenrui Dai, Yitong Li","doi":"10.1080/17512549.2022.2067229","DOIUrl":"https://doi.org/10.1080/17512549.2022.2067229","url":null,"abstract":"ABSTRACT At present, the thermal comfort evaluation index has not been differentiated for different types of people, and the indoor thermal environment of the residences in rural areas of cold climate, China, urgently need attention. In this study, indoor living environment of rural residences in Hebei and Tianjin were measured to obtain typical residential forms, building thermal parameters and thermal comfort parameters of the elderly. At the same time, 152 elderly people were investigated by questionnaire to obtain the subjective evaluation such as thermal sensation. In addition, the optimization suggestions of plane structure, building envelope and window to wall ratio were put forward through software simulation. The results show that the thermal neutral temperature of the rural elderly people in cold zone is 16.74°C, and they are more sensitive to the temperature below 15.67°C and more slowly to the temperature above 15.67°C. The recommended thickness of wall insulation layer is 80–100 mm, and that of roof insulation layer is 60–80 mm. Double layer glass should be used. Wood window frame has better thermal insulation effect than aluminium frame, and the recommended window to wall ratio is 0.28.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"16 1","pages":"612 - 642"},"PeriodicalIF":2.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42555613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-03DOI: 10.1080/17512549.2022.2079001
Pouyan Alaei, B. Ghasemi, A. Raisi, A. Torabi
ABSTRACT The unique thermal energy storage system (TESS) as an auxiliary system of solar water heater has a critical part to demonstration in preserving and efficiently utilizing energy, resolving demand-supply mismatches, and boosting the efficiency of energy systems. In this research, a suitable Nano enhanced-Composite Phase Change Material (NCPCM) was prepared to utilize in two model of metal cylinders. First of all, an experimental test for determination of melting point has been investigated by temperature variation test (TVT) method for NCPCM based on paraffin component. Then, two nanomaterial types (TiO2 and CuO) mixed at 50%/50% with two concentrations (0.6% and 0.12%) were dispersed with slack paraffin to provide a total of four experiments to compare the average temperature evolution and exergy efficiency. A theoretical framework for exergy analysis give away that use of nano composites in PCMs will improve efficiency rather than a single nanomaterial. However, to assess capability of this system to integrate with solar water heater, if there is no solar radiation limitation, NCPCM with 1.2% wt. will be a better choice with higher cylinders diameter (HD-Cylinders). Neverthless, for climates with limited time in storage energy, NCPCM with 1.2% wt. and lower cylinders diameter (LD-Cylinders) is our best suggestion for maximum efficiency that can be used during the peak solar energy period. Therefore, by considering NCPCM and integrating this novel study with solar technologies as a reliable heat source, outline is an excellent way for saving energy consumption in buildings at any remote area.
{"title":"A pilot project of TESS equipped with two models of encapsulation for nano-enhanced organic PCMs","authors":"Pouyan Alaei, B. Ghasemi, A. Raisi, A. Torabi","doi":"10.1080/17512549.2022.2079001","DOIUrl":"https://doi.org/10.1080/17512549.2022.2079001","url":null,"abstract":"ABSTRACT\u0000 The unique thermal energy storage system (TESS) as an auxiliary system of solar water heater has a critical part to demonstration in preserving and efficiently utilizing energy, resolving demand-supply mismatches, and boosting the efficiency of energy systems. In this research, a suitable Nano enhanced-Composite Phase Change Material (NCPCM) was prepared to utilize in two model of metal cylinders. First of all, an experimental test for determination of melting point has been investigated by temperature variation test (TVT) method for NCPCM based on paraffin component. Then, two nanomaterial types (TiO2 and CuO) mixed at 50%/50% with two concentrations (0.6% and 0.12%) were dispersed with slack paraffin to provide a total of four experiments to compare the average temperature evolution and exergy efficiency. A theoretical framework for exergy analysis give away that use of nano composites in PCMs will improve efficiency rather than a single nanomaterial. However, to assess capability of this system to integrate with solar water heater, if there is no solar radiation limitation, NCPCM with 1.2% wt. will be a better choice with higher cylinders diameter (HD-Cylinders). Neverthless, for climates with limited time in storage energy, NCPCM with 1.2% wt. and lower cylinders diameter (LD-Cylinders) is our best suggestion for maximum efficiency that can be used during the peak solar energy period. Therefore, by considering NCPCM and integrating this novel study with solar technologies as a reliable heat source, outline is an excellent way for saving energy consumption in buildings at any remote area.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"16 1","pages":"643 - 668"},"PeriodicalIF":2.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45906271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-17DOI: 10.1080/17512549.2022.2108142
Lakmini Rangana Senarathne, Gaurav Nanda, R. Sundararajan
ABSTRACT Design parameters of a building play a major role in its energy consumption. Towards this, we studied the energy efficiency of buildings using the association and dependence of input variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution, to the output variables-heating load (HL) and cooling load (CL). Bayesian network, a supervised machine learning model, was used to identify dependencies between variables. UCI energy efficiency dataset (768) with eight-labelled inputs was used to make predictions with 10-fold cross validation. The Bayesian network was chosen to identify the most impactful input parameters. Seven search algorithms to determine the Bayesian network structure based on training data were considered to analyze the best-performing algorithm for predicting the relationship between nodes. Among those, Tabu search (82.81% and 81.77%) and Simulated annealing (82.68% and 81.38%) performed best with highest accuracies for both HL and CL. In addition, it is found that reduced heights of buildings will have a very high-energy efficiency level for both HL and CL. Reduced glazing areas will have a high-energy efficiency level for HL. These findings could be used to build real-world higher energy efficient structures.
{"title":"Influence of building parameters on energy efficiency levels: a Bayesian network study","authors":"Lakmini Rangana Senarathne, Gaurav Nanda, R. Sundararajan","doi":"10.1080/17512549.2022.2108142","DOIUrl":"https://doi.org/10.1080/17512549.2022.2108142","url":null,"abstract":"ABSTRACT Design parameters of a building play a major role in its energy consumption. Towards this, we studied the energy efficiency of buildings using the association and dependence of input variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution, to the output variables-heating load (HL) and cooling load (CL). Bayesian network, a supervised machine learning model, was used to identify dependencies between variables. UCI energy efficiency dataset (768) with eight-labelled inputs was used to make predictions with 10-fold cross validation. The Bayesian network was chosen to identify the most impactful input parameters. Seven search algorithms to determine the Bayesian network structure based on training data were considered to analyze the best-performing algorithm for predicting the relationship between nodes. Among those, Tabu search (82.81% and 81.77%) and Simulated annealing (82.68% and 81.38%) performed best with highest accuracies for both HL and CL. In addition, it is found that reduced heights of buildings will have a very high-energy efficiency level for both HL and CL. Reduced glazing areas will have a high-energy efficiency level for HL. These findings could be used to build real-world higher energy efficient structures.","PeriodicalId":46184,"journal":{"name":"Advances in Building Energy Research","volume":"16 1","pages":"780 - 805"},"PeriodicalIF":2.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44899422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：