Space Structures for Low-stress Environments

2005 ◽  
Vol 20 (3) ◽  
pp. 127-133 ◽  
Author(s):  
Bezaleel S. Benjamin

This paper examines the criteria for the architectural design of space structures in low-stress environments, where stress-carrying ability is no longer a relevant consideration in the design of the space structure. In such environments, ergonomics and minimum surface criteria can lead to the most efficient design of the space structure. Based on these two criteria, the paper then considers the architectural design of a space tunnel and an interchange station. In direction-controlled artificial gravity environments, the nesting of ergonomic shapes, for minimum surface area, can lead to even more cost-effective design. 3–D computer graphics are used to display the results of this research.

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.


Author(s):  
Roger Hitchin

Policies to reduce carbon emissions are leading to substantial changes in the demand for electricity and to the structure of electricity supply systems, which will alter the cost structure of electricity supply. This can be expected to result in corresponding changes to the price structure faced by customers. This note is an initial exploration of how possible new price structures may impact on HVAC system and building design and use. Changes in the price structure of electricity supply (separately from changes in price levels) can significantly affect the cost-effective design and operation of building services systems; especially of heating and cooling systems. The nature and implications of these changes can have important implications for future system design and operation.


2000 ◽  
Vol 11 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Qiao Chunming ◽  
Mei Yousong ◽  
Yoo Myungsik ◽  
Zhang Xijun

2011 ◽  
Vol 94-96 ◽  
pp. 587-593 ◽  
Author(s):  
Jin Liang ◽  
Su Duo Xue ◽  
Xiong Yan Li

Abstract.The fire smoke is one of the most important factors for the fire temperature field. Once the fire smoke has been exhausted effectively, the fire temperature will be reduced and the fire-resistance performance of steel structures may be improved as well. However, the research on the fire temperature in the space structures is almost bold, which could lead to the theoretical analysis result on fire temperature is quite different from the real condition. Accordingly, the air temperature condition on fire for large-space structures has been analyzed. Taken into account smoke ventilation, the empirical formula for air temperature in large space structure under fire has been modified.


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