A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Intan Azmira Wan Abdul Razak ◽  
◽  
Nik Nur Atira Nik Ibrahim ◽  
Izham Zainal Abidin ◽  
Yap Keem Siah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Price Forecasting ◽  
Medium Term ◽  
Bacterial Foraging Optimization ◽  
Electricity Price Forecasting ◽  
Method Of Least Square ◽  
Bacterial Foraging Optimization Algorithm

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>

scholarly journals A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
S. Zheng ◽  
A. N. Jiang ◽  
X. R. Yang ◽  
G. C. Luo
Keyword(s):  
Support Vector Machine ◽  
Rock Mass ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Bacterial Foraging Optimization ◽  
Mass Classification ◽  
Bacterial Foraging Optimization Algorithm

Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time-consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA-LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.

A Recursive Gradient Method of Least Square Support Vector Machine and its Application

2013 ◽  
Vol 339 ◽  
pp. 349-354
Author(s):  
Xiang Yong Luo ◽  
Wei Min Lv ◽  
Zhao Qing Song ◽  
Shi Wei Jiang
Keyword(s):  
Support Vector Machine ◽  
Parameter Estimation ◽  
Gradient Method ◽  
Least Square ◽  
Support Vector ◽  
Recursive Method ◽  
Online Identification ◽  
Method Of Least Square ◽  
Bad Data

Least Square Support Vector Machine (LS-SVM) is an important machine of Support Vector Machine (SVM). But this method can not be used for online identification, and maybe lead to calculation inflation. A gradient recursive method of LS-SVM is presented by combining the LS-SVM method with the gradient method. This method can overcome the influence of bad data to the parameter estimation, has a stronger robustness, and improves the calculation speed of LS-SVM. The presented method is applied to the modeling of chaotic series. The simulation example validates the validity of the presented method.

Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1956007
Author(s):  
Zhigao Zeng ◽  
Lianghua Guan ◽  
Wenqiu Zhu ◽  
Jing Dong ◽  
Jun Li
Keyword(s):  
Face Recognition ◽  
Kernel Function ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Function Selection ◽  
Dispersal Probability ◽  
Bacterial Foraging Optimization Algorithm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.

Support vector machine for day ahead electricity price forecasting

2015 ◽  
Author(s):  
Intan Azmira binti Wan Abdul Razak ◽  
Izham bin Zainal Abidin ◽  
Yap Keem Siah ◽  
Titik Khawa binti Abdul Rahman ◽  
M. Y. Lada ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Electricity Price Forecasting
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