Metering equipment running error estimation model based on genetic optimized LM algorithm

2021 ◽  
pp. 1-9
Author(s):  
Liang Chen ◽  
Youpeng Huang ◽  
Tao Lu ◽  
Sanlei Dang ◽  
Zhengmin Kong
Keyword(s):  
Real Time ◽  
Error Estimation ◽  
Electricity Consumption ◽  
Current Method ◽  
Smart Meter ◽  
Estimation Model ◽  
Conservation Of Energy ◽  
Model Based ◽  
Regular Sampling ◽  
Lm Algorithm

The current method of smart meter verification relies on manual regular sampling inspection, which is heavy in workload and poor in real-time, and can’t fully monitor all the equipments. Therefore, a remote real-time error monitoring algorithm is indispensable. We propose a smart meter error estimation model based on genetic optimized Levenberg-Marquarelt (LM) algorithm. Firstly, based on the law of conservation of energy, the relationship between smart meter error and electricity consumption is established. Then, LM algorithm is optimized based on genetic algorithm and used to estimate the operating error of electricity meter. Finally, we used the actual data of the pilot cities in a province for the experiment. The results show that the proposed method can effectively improve the accuracy of smart meter error estimation.

scholarly journals Real-time bus travel speed estimation model based on bus GPS data

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401667816 ◽  
Author(s):  
Jiancheng Weng ◽  
Chang Wang ◽  
Hainan Huang ◽  
Yueyue Wang ◽  
Ledian Zhang
Keyword(s):  
Real Time ◽  
Travel Speed ◽  
Gps Data ◽  
Speed Estimation ◽  
Estimation Model ◽  
Model Based

Towards real-time fire data synthesis using numerical simulations

2021 ◽  
pp. 073490412199344
Author(s):  
Wolfram Jahn ◽  
Frane Sazunic ◽  
Carlos Sing-Long
Keyword(s):  
Real Time ◽  
Computational Fluid Dynamic ◽  
Dynamic Models ◽  
Fluid Dynamic ◽  
Near Field ◽  
Computing Time ◽  
Temperature Correction ◽  
Dynamic Simulations ◽  
Conservation Of Energy ◽  
Fire Scenarios

Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

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