No abstract

No abstract

Aiming at the optimal scheduling of cogeneration microgrid, a microgrid structure including wind energy, energy storage device, solar thermal power station, gas turbine and waste heat boiler is proposed. Based on the uncertainty of wind power output and load forecasting, according to beta distribution wind power output and normal distribution load forecasting, the CHP microgrid optimal scheduling model of photothermal power station is established and solved by genetic algorithm. Finally, the minimum operation cost of the system under different confidence levels is discussed. The results show that the minimum operating cost of the system can be significantly reduced by adding wind power generation into the microgrid structure, and the relationship between the confidence level of load forecasting and the minimum operating cost of the system is studied.

In recent years, the penetration rate of smartphones has gradually completed, artificial intelligence is the cutting-edge technology that can trigger disruptive changes. Deep learning neural networks are also starting to appear on mobile devices. In order to obtain better performance, more complex networks need to be designed, and the corresponding models, computation and storage space are increasing, however, the challenges of resource allocation and energy consumption still exist in mobile. The techniques for compressing deep learning models are quite important, and this paper studies a series of related literatures. This paper reviews deep learning-based deep neural network compression techniques and introduces the key operational points of knowledge extraction and network model on the learning performance of Resolution-Aware Knowledge Distillation. In this paper, a low-rank decomposition algorithm is evaluated based on sparse parameters and rank using the extended BIC for tuning parameter selection. This paper discusses the reduction of redundancy in the fully connected and constitutive layers of the training network model by pruning strategies.Moreover, this paper presents the quantization techniques and a neural network that quantifies weights and activations by applying differentiable nonlinear functions.

No abstract