Analysis of energy management for heating, ventilating and air-conditioning systems

AbstractIn the office buildings, large energy is consumed due to poor thermal performance and low efficiencies of HVAC systems. A cooling load calculation is a basis for the design of building cooling systems. The current design methods are usually based on deterministic cooling loads, which are obtained by using design parameters. However, these parameters contain uncertainties, and they will be different from that used in the design calculation when the cooling system is put in use. The actual cooling load profile will deviate from that predicted in design. A modified bin method was used in this paper to optimize the energy efficiency ratio (EER). A design optimization method is proposed by considering uncertainties related to the cooling load calculation. Impacts caused by the uncertainties of seven factors are considered, including the outdoor weather conditions and internal heat sources. The cooling load distribution is analyzed. Comparison between the modified bin method and CLTD/SCL/CLF method is also conducted. With the distributions of their energy consumption, decision makers can select the optimal configuration based on quantified confidence. According to the economic benefits and energy efficiency ratio, using modified bin method will increase the overall energy efficiency ratio by 45.57%

Performance evaluation for solar liquid desiccant air dehumidification system

AbstractIn this paper, a solar liquid desiccant air conditioning (SLDAC) system has been studied. The effect of changing evacuated tube collector area on the performance of the SLDAC system was fulfillment. This inquest was done over all a year in Borg Al-Arab city located in the Northern region of Egypt. Meteorological data, such as hourly average solar radiations and temperatures, were needed to achieve this research. The hourly cooling loads were determined by using Hourly Analysis Program (HAP) 4.7. These loads are wall, illumination, people, and equipment loads. Then, the hourly differences of different parameters such as amount of water absorbed in conditioner, amount of water desorbed in regenerator, hot water temperature and coefficient of the performance were calculated.In addition, the maximum solar thermal energy was determined to meet the regeneration demand according to the hourly average solar radiation data. For 220m2 evacuated tube collector area, the maximum required heat energy is obtained as 38,286kWh on December, while using solar energy, will save energy by 30.28% annual value

RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model

Satellite imagery generation and super-resolution are pivotal tasks in remote sensing, demanding high-quality, detailed images for accurate analysis and decision-making. In this paper, we propose an innovative and lightweight approach that employs two-stage diffusion models to gradually generate high-resolution Satellite images purely based on text prompts. Our innovative pipeline comprises two interconnected diffusion models: a Low-Resolution Generation Diffusion Model (LR-GDM) that generates low-resolution images from text and a Super-Resolution Diffusion Model (SRDM) conditionally produced. The LR-GDM effectively synthesizes low-resolution by (computing the correlations of the text embedding and the image embedding in a shared latent space), capturing the essential content and layout of the desired scenes. Subsequently, the SRDM takes the generated low-resolution image and its corresponding text prompts and efficiently produces the high-resolution counterparts, infusing fine-grained spatial details and enhancing visual fidelity. Experiments are conducted on the commonly used dataset, Remote Sensing Image Captioning Dataset (RSICD). Our results demonstrate that our approach outperforms existing state-of-the-art (SoTA) models in generating satellite images with realistic geographical features, weather conditions, and land structures while achieving remarkable super-resolution results for increased spatial precision

Reduced erbium-doped ceria nanoparticles: one nano-host applicable for simultaneous optical down- and up-conversions

This paper introduces a new synthesis procedure to form erbium-doped ceria nanoparticles (EDC NPs) that can act as an optical medium for both up-conversion and down-conversion in the same time. This synthesis process results qualitatively in a high concentration of Ce(3+) ions required to obtain high fluorescence efficiency in the down-conversion process. Simultaneously, the synthesized nanoparticles contain the molecular energy levels of erbium that are required for up-conversion. Therefore, the synthesized EDC NPs can emit visible light when excited with either UV or IR photons. This opens new opportunities for applications where emission of light via both up- and down-conversions from a single nanomaterial is desired such as solar cells and bio-imaging

Eucalyptus

In Egypt, the River Red Gum (Eucalyptus camaldulensis) is a well-known tree and is highly appreciated by the rural and urban dwellers. The role of Eucalyptus trees in the ecology of Cryptococcus neoformans is documented worldwide. The aim of this survey was to show the prevalence of C. neoformans during the flowering season of E. camaldulensis at the Delta region in Egypt. Three hundred and eleven samples out of two hundred Eucalyptus trees, including leaves, flowers, and woody trunks, were collected from four governorates in the Delta region. Thirteen isolates of C. neoformans were recovered from Eucalyptus tree samples (4.2%). Molecular identification of C. neoformans was done by capsular gene specific primer CAP64 and serotype identification was done depending on LAC1 gene. This study represents an update on the ecology of C. neoformans associated with Eucalyptus tree in Egyptian environment

STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map

Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one, and some groupings might produce performance degradation due to negative interference between tasks. Furthermore, existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on hand-crafted features, specifically Data Maps, which capture the training behavior for each classification task during the MTL training. We experiment with the method demonstrating its effectiveness, even on an unprecedented number of tasks (up to 100).Comment: Accepted submission to DMLR workshop @ ICML 2