Review of onsite temperature and solar forecasting models to enable better building design and operations

Dong, Bing; Widjaja, Reisa; Wu, Wenbo; Zhou, Zhi

doi:10.1007/s12273-020-0759-2

Cited by 16 publications

(3 citation statements)

References 93 publications

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“…Accurate weather forecasting, like eWeather HD, can reduce weather-related accidents (Moohialdin et al ., 2020; Smith et al ., 2021; Schuldt et al ., 2021). Real-time weather data, available through applications such as eWeather HD, provide severe weather alerts, enabling project managers and contractors to take safety measures (Dong et al ., 2021; Frackleton et al ., 2019; Majumdar et al ., 2021). Apps enhance site safety and protect workers by offering weather updates and by reducing weather-related accidents (Lagasio et al ., 2020).…”

Section: Findings and Discussionmentioning

confidence: 99%

Apps as partial replacement for robotics and automation systems in construction health and safety management

Yankah,

Adjei,

Tieru

2024

FEBE

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PurposeRobotics and automation are successful in construction, health and safety, but costs and expertise hinder their use in developing nations. This study examined mobile apps as a more accessible and affordable alternative.Design/methodology/approachThis descriptive study explored the use of mobile apps in construction, health and safety management. It used a literature review to identify their availability, accessibility, and capabilities. The study consisted of four five stages: searching for relevant apps, selecting them based on versatility, examining their specific functions, removing untested apps and discussing their functions based on empirical studies.FindingsA comprehensive literature review identified 35 mobile apps that are relevant to health and safety management during construction. After rigorous analysis, eight apps were selected for further study based on their relevance, user friendliness and compliance with safety standards. These apps collectively serve 28 distinct functions, including first-aid training and administration, safety compliance and danger awareness, safety education and training, hazard detection and warnings.Practical implicationsThis study suggests that mobile apps can provide a cost-effective and readily accessible alternative to robotics and automation in health and safety management in construction. Further research is needed to accurately assess the efficacy of these apps in real-world conditions.Originality/valueThis study explored the use of apps in health and safety management, highlighting their diverse capabilities and providing a framework for project managers, contractors and safety officers to select suitable apps.

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Section: Findings and Discussionmentioning

confidence: 99%

Apps as partial replacement for robotics and automation systems in construction health and safety management

Yankah,

Adjei,

Tieru

2024

FEBE

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“…The MAE and RMSE for air temperature are 0.98 °C and 1.22 °C, respectively, and are 1.23 °C and 1.69 °C for the surface temperature of the glass door, which are within the permissible error range [ 42 ]. Therefore, the accuracy of the simulation was verified with experiments.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Thermal Insulation Thickness for a Container House in the Yanqing Zone of the Beijing 2022 Olympic and Paralympic Winter Games

Tong

Yang

et al. 2022

IJERPH

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A large number of temporary housings (THs) were used in the Yanqing zone of the Beijing 2022 Olympic and Paralympic Winter Games. Taking a kind of container house (CH) used in Yanqing zone as a model, the objective of this paper is to analyze the effect of insulation thickness on heating energy consumption and corresponding carbon emission. The effect of service life of THs on economic thickness was also discussed. The simulation model was developed using EnergyPlus and the heating energy consumption with different insulation materials was simulated based on the meteorological parameters of the top of Xiaohaituo Mountain (2177.5 m) and the Olympic/Paralympic Village (950 m) in Yanqing zone. In the simulation process, the thermal insulation performance of the CH was enhanced with reference to the requirements of GB/T 51350-2019 Technical Standard for Nearly Zero Energy Buildings (NZEB) on one hand. Additionally, the insulation performance was evaluated in terms of payback period and carbon emission. On the other hand, the economic thickness of different insulation materials (rock wool (RW), extruded polystyrene (XPS), polyurethane (PU)) and the high performance vacuum insulation panel (HVIP)) for different service lives of CH was studied. Results show that the U-values of the envelope meeting the NZEB standard can decrease approximately 21.4–32.8% of the heating energy consumption, compared with the original envelope. When the service life of CH is extended to 20 years, the carbon emission is reduced by 18.5% and 29.5%. The payback period of HVIP is longest, up to 31.4 a, and the results of economic thickness show that when the service life of the CH ranges from 1 year to 20 years, the economic thickness range of RW is 47–235 mm, XPS is 41–197 mm, PU is 33–149 mm and HVIP is 4–18 mm at the altitude of 2177.5 m. At the altitude of 950 m, the economic thickness range of RW is 28–158 mm, XPS is 26–131 mm, PU is 25–118 mm, and HVIP is 2–12 mm. From an economic point of view, the service life of a CH has a significant impact on the choice of insulation thickness.

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“…Moreover, the proposed model could avoid the influence of the input sequence long time step and was able to extract information from the features that most affect the energy forecasting. e authors suggested the consideration of weather factors [89] and electricity price policy supplementary data for their future work.…”

Section: Residential Building Loadmentioning

confidence: 99%

A Survey on Deep Learning for Building Load Forecasting

Patsakos

Vrochidou

Papakostas

2022

Mathematical Problems in Engineering

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Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.

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Review of onsite temperature and solar forecasting models to enable better building design and operations

Cited by 16 publications

References 93 publications

Apps as partial replacement for robotics and automation systems in construction health and safety management

Apps as partial replacement for robotics and automation systems in construction health and safety management

Analysis of Thermal Insulation Thickness for a Container House in the Yanqing Zone of the Beijing 2022 Olympic and Paralympic Winter Games

A Survey on Deep Learning for Building Load Forecasting

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