Research on optimization of energy consumption monitoring point layout on user side

Zheng, Jiajian; Han, Guangjie; Yang, Shouyong; Tan, Shujuan

doi:10.1109/icaica52286.2021.9498073

Cited by 2 publications

(1 citation statement)

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“…In industrial production, the consumption of electric energy is very large. Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points is of great significance, and monitoring points are selected according to the fluctuation coefficients of energy efficiency [2]. In order to overcome the complexity of sensor equipment in data acquisition, transmission, storage and analysis, and to achieve the purpose of condition monitoring, estimation and control, a load monitoring system for chemical enterprises has been proposed to collect energy consumption data and analyze energy consumption, and an Elman neural network based on the sparrow search algorithm was proposed to predict the change and distribution trends of electricity consumption in the future production cycles of enterprises [3].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System

Xiao,

Jin,

Huang

et al. 2023

Electronics

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In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user’s electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately 2% and reduces the system delay by approximately 16%, so that the user’s electricity consumption information can be analyzed and processed faster.

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Section: Introductionmentioning

confidence: 99%