Prediction model on disturbance of maintenance operation during real‐time pricing adaptive control for building air‐conditioners

2019 ◽  
Vol 14 (8) ◽  
pp. 1219-1225 ◽  
Author(s):  
Shun Matsukawa ◽  
Masanori Takehara ◽  
Hideyuki Otsu ◽  
Junji Morikawa ◽  
Takashi Inaba ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Prediction Model ◽  
Real Time ◽  
Air Conditioners ◽  
Real Time Pricing

Dynamic SMOTE training of neural networks used in real‐time pricing control for building air‐conditioners

2019 ◽  
Vol 14 (11) ◽  
pp. 1727-1728
Author(s):  
Shun Matsukawa ◽  
Takuya Nakayama ◽  
Chuzo Ninagawa ◽  
Junji Morikawa
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Air Conditioners ◽  
Real Time Pricing ◽  
Pricing Control

Neural network prediction model for a real-time data transmission

2006 ◽  
Vol 15 (3-4) ◽  
pp. 373-382 ◽  
Author(s):  
Kil To Chong ◽  
Sung Goo Yoo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Real Time ◽  
Data Transmission ◽  
Time Data ◽  
Real Time Data ◽  
Neural Network Prediction ◽  
Network Prediction

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

scholarly journals Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4597
Author(s):  
Zi-Xuan Yu ◽  
Meng-Shi Li ◽  
Yi-Peng Xu ◽  
Sheraz Aslam ◽  
Yuan-Kang Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Positive Impact ◽  
Environmental Aspects ◽  
Component Size ◽  
Cost Of Energy ◽  
Four Seasons ◽  
Demand Response Program ◽  
Real Time Pricing

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

scholarly journals Development of an Institution-Specific Readmission Risk Prediction Model for Real-time Prediction and Patient-Centered Interventions

2021 ◽  
Author(s):  
Ann-Marcia C. Tukpah ◽  
Eric Cawi ◽  
Laurie Wolf ◽  
Arye Nehorai ◽  
Lenise Cummings-Vaughn
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Patient Centered ◽  
Time Prediction ◽  
Readmission Risk
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