Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Author(s):  
Muhammad Waseem ◽  
Zhenzhi Lin ◽  
Shengyuan Liu ◽  
Zhang Jinai ◽  
Mian Rizwan ◽  
...  
2019 ◽  
Vol 158 ◽  
pp. 3411-3416 ◽  
Author(s):  
Yao Huang ◽  
Yue Yuan ◽  
Huanxin Chen ◽  
Jiangyu Wang ◽  
Yabin Guo ◽  
...  

Author(s):  
K Gwirtz ◽  
M Morzfeld ◽  
A Fournier ◽  
G Hulot

Summary We study predictions of reversals of Earth’s axial magnetic dipole field that are based solely on the dipole’s intensity. The prediction strategy is, roughly, that once the dipole intensity drops below a threshold, then the field will continue to decrease and a reversal (or a major excursion) will occur. We first present a rigorous definition of an intensity threshold-based prediction strategy and then describe a mathematical and numerical framework to investigate its validity and robustness in view of the data being limited. We apply threshold-based predictions to a hierarchy of numerical models, ranging from simple scalar models to 3D geodynamos. We find that the skill of threshold-based predictions varies across the model hierarchy. The differences in skill can be explained by differences in how reversals occur: if the field decreases towards a reversal slowly (in a sense made precise in this paper), the skill is high, and if the field decreases quickly, the skill is low. Such a property could be used as an additional criterion to identify which models qualify as Earth-like. Applying threshold-based predictions to Virtual Axial Dipole Moment (VADM) paleomagnetic reconstructions (PADM2M and Sint-2000) covering the last two million years, reveals a moderate skill of threshold-based predictions for Earth’s dynamo. Besides all of their limitations, threshold-based predictions suggests that no reversal is to be expected within the next 10 kyr. Most importantly, however, we show that considering an intensity threshold for identifying upcoming reversals is intrinsically limited by the dynamic behavior of Earth’s magnetic field.


Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1320
Author(s):  
Julia Sophie Böke ◽  
Daniel Kraus ◽  
Thomas Henkel

Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.


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