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A fabric air dispersion system (FADS) is a type of flexible air supply system that integrates air transmission and distribution. This innovative system has the potential to address common issues such as uneven air supply and surface condensation, which are often associated with traditional ventilation systems. Existing numerical simulation studies on fiber ducts have encountered problems with mesh generation and simulation accuracy. This work develops a simulation method based on the equivalent discounting method to overcome these challenges. The proposed method is utilized to investigate the flow and heat transfer characteristics inside fiber ducts while also examining the effects of various shapes and opening configurations. The findings indicate that the temperature rise inside the duct is positively correlated with flow rate, with higher temperatures resulting from faster flow speeds. The temperature rise of FADS with four rows of openings increased by 0.4 k compared to other opening methods. Additionally, the study shows that increasing the number of rows of openings in the fiber duct leads to a faster decay of flow velocity and a higher temperature rise. At the same time, increasing the number of openings in the duct slightly reduces flow velocity while slightly increasing the temperature rise. The presence of more fiber duct elbows leads to greater local resistance, which accelerates the decay of the flow velocity and increases the temperature rise. Compared to the "1"-shaped FADS, the temperature rises of the "L"-shaped and "U"-shaped systems have increased by 0.9 k and 2.9 k, respectively. [ABSTRACT FROM AUTHOR]

With the coming of the 2022 Beijing Winter Olympic Games, China's artificial ice rink construction will be in rapid development. A parametric evaluation of the cooling pipe in a direct evaporation rink was performed by numerical simulation. The results showed that the influence of the temperature of the antifreeze pipe on the ice surface temperature can be ignored. The evaporation temperature of the working medium in the cooling pipe is between −32 °C and −22.4 °C to ensure the ice surface temperature is between −5 °C and −3 °C. With the increase in the cooling pipe spacing, the required evaporation temperature of the working medium in the cooling pipe and the uniformity of the ice surface temperature decreased. The required evaporation temperature of the working medium in the cooling pipe decreases by 1.2–1.5 °C for every 10 mm increment of spacing. With the increase in the cooling pipe diameter, the required evaporation temperature of the working medium in the cooling pipe and the uniformity of the ice surface temperature increase. The required evaporation temperature of the working medium in the cooling pipe increases by 2.2–2.9 °C for every 5 mm increment of diameter. The maximum temperature difference of ice surface temperature ranged from 0.004 °C to 0.111 °C. [ABSTRACT FROM AUTHOR]

As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data. [ABSTRACT FROM AUTHOR]

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models' effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters. [ABSTRACT FROM AUTHOR]