scholarly journals Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 231 ◽  
Author(s):  
Dongxiao Niu ◽  
Yan Li ◽  
Shuyu Dai ◽  
Hui Kang ◽  
Zhenyu Xue ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Construction Projects ◽  
Power Grid ◽  
Least Square ◽  
Support Vector ◽  
Sustainability Evaluation ◽  
Grid Construction

scholarly journals Shape Optimization of Labyrinth Seals to Improve Sealing Performance

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 92
Author(s):  
Yizhen Zhao ◽  
Chunhua Wang
Keyword(s):  
Support Vector Machine ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Labyrinth Seal ◽  
Least Square ◽  
Design Parameters ◽  
Support Vector ◽  
Labyrinth Seals ◽  
Gas Leakage ◽  
Expansion Angle

To reduce gas leakage, shape optimization of a straight labyrinth seal was carried out. The six design parameters included seal clearance, fin width, fin height, fin pitch, fin backward, and forward expansion angle. The CFD (Computational Fluid Dynamics) model was solved to generate the training and testing samples for the surrogate model, which was established by the least square support vector machine. A kind of chaotic optimization algorithm was used to determine the optimal design parameters of the labyrinth seal. As seal clearance, fin width, fin height, fin pitch, fin backward and forward expansion angles are 0.2 mm, 0.1 mm, 7 mm, 9 mm, 0°, and 15°, the discharge coefficient can reach its minimum value in the design space. The chaotic optimization algorithm coupled with least square support vector machine is a promising scheme for labyrinth seal optimization.

scholarly journals An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 748
Author(s):  
Intan Azmira Wan Abdul Razak ◽  
Izham Zainal Abidin ◽  
Yap Keem Siah ◽  
Aidil Azwin Zainul Abidin ◽  
Titik Khawa Abdul Rahman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Optimization Algorithm ◽  
Forecast Accuracy ◽  
Electricity Price ◽  
Least Square ◽  
Support Vector ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

<span lang="EN-US">Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.</span>

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