By Bachir Achour, Qiyan Wu
This publication holds the court cases of the 2016 foreign convention on increase in strength and setting study (ICAEER 2016), which used to be hung on August 12-14, 2016 in Guangzhou, China.
ICAEER 2016 introduced jointly cutting edge lecturers and commercial specialists within the box of strength and setting study to a standard discussion board. the first aim of the convention used to be to advertise study and developmental actions in power and atmosphere learn and one other objective is to advertise medical details interchange among researchers, builders, engineers, scholars, and practitioners operating everywhere in the international. The conference is held each year to make it a terrific platform for individuals to proportion perspectives and stories in strength and setting learn and comparable areas.
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Extra info for Advances in Energy and Environment Research: Proceedings of the International Conference on Advances in Energy and Environment Research (ICAEER2016), Guangzhou City, China, August 12-14, 2016
4) (5) 3 where n is the number of training data samples, xi is the actual value, and xˆi is the predicted value. GA–LSSVR MODEL APPLICATION Week max load series of some areas are used in this paper to testify to the proposed forecasting model performance. The week max load series are shown in Figure 2. 4. Genetic operator: the processes carried out by using a genetic operator include selection, crossover, and mutation. A standard tournament selection method is employed to select 8 chromosomes from the current population.
Advances in Energy and Environment Research – Achour & Wu (Eds) © 2017 Taylor & Francis Group, London, ISBN 978-1-138-62682-9 Load prediction based on support vector regression with genetic algorithms Min Zou School of Electronics and Electricity Engineering, Wuhan Textile University, Wuhan, China ABSTRACT: Load prediction is an important activity in either real-time or day-ahead electricity markets. In this paper, a combination approach based on Least Square Support Vector Regression (LSSVR) and Genetic Algorithm (GA) parameters’ optimization is proposed for load prediction.
Kernel function: the kernel function is important in the LSSVR model and is used to construct a nonlinear decision hyper-surface in the LSSVR input space. The RBF kernel is the most typical of all kernel functions and exhibits the same performance under some conditions with other kernel functions, such as linear polynomial and sigmoid kernel functions. 2. Regularization parameter γ : γ determines the trade-off cost between minimizing the training error and minimizing the model’s complexity. 3. Bandwidth of the kernel function ( σ 2 ): σ 2 represents the variance of the Gaussian kernel function.