This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R2 of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage.
Green building technologies (GBTs) play an important role in carbon emission reduction in the building sector. China is currently in the booming phase of green buildings construction and numerous studies have been conducted on green building technologies, especially on the potential of reducing buildings' energy consumption and carbon emissions. This paper provides a comprehensive overview of various GBTs, including high performance envelope, lighting and daylighting, natural ventilation, HVAC (heating, ventilation and air conditioning) system and the utilization of renewable energy. After describing the phases of the building life cycle and the calculation method of building carbon emissions, the literature review focuses on the applications of GBTs in different climate areas in China as well as the main findings and innovations on their carbon reduction potentials. Finally, recommendations for GBTs development are proposed based on the existing researches to facilitate carbon neutrality in the building sector.
This paper proposes a decentralized cooling system combined with a traditional computer room air conditioning unit and server-level heat pipe exchangers for thermal environment optimization in a data center. Two cooling strategies, with heat exchangers installed above and below the servers respectively, are proposed and compared with the original CRACs system in terms of thermal environment. The simulation results of the original data center model are in good agreement with the on-site measurement results, and thus its reliability can be validated. The results show that a decentralized cooling system can effectively improve the thermal environment in data centers. To obtain the highest cooling efficiency, altogether 18 cases, where heat pipe exchangers were installed at different locations and heights, are analyzed and compared. The results show that the thermal environment is optimal when heat pipe exchangers are installed 0.01 m below each server. The local hotspot temperature is reduced by 6.8 °C, and the temperature distribution of the rack is the most uniform, which can effectively reduce the heat accumulation in data centers.
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