scholarly journals An artificial immune network based novel approach to predict short term load forecasting

2017 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Load Forecasting ◽  
Immune Network ◽  
Artificial Immune ◽  
Short Term ◽  
Novel Approach ◽  
Artificial Immune Network ◽  
Short Term Load Forecasting

A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

scholarly journals Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Taiyong Li ◽  
Zijie Qian ◽  
Ting He
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Evaluation Criteria ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Energy Forecasting ◽  
Multiple Kernel ◽  
Novel Approach ◽  
Short Term Load Forecasting ◽  
Learning Machine

Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

Novel approach considering load-relative factors in short-term load forecasting

2004 ◽  
Vol 70 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Chongqing Kang ◽  
Xu Cheng ◽  
Qing Xia ◽  
Yonghao Huang ◽  
Feng Gao
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Novel Approach ◽  
Short Term Load Forecasting ◽  
Relative Factors

A novel approach to short-term load forecasting using fuzzy neural networks

1998 ◽  
Vol 13 (2) ◽  
pp. 480-492 ◽  
Author(s):  
S.E. Papadakis ◽  
J.B. Theocharis ◽  
S.J. Kiartzis ◽  
A.G. Bakirtzis
Keyword(s):  
Neural Networks ◽  
Load Forecasting ◽  
Fuzzy Neural Networks ◽  
Short Term ◽  
Novel Approach ◽  
Short Term Load Forecasting ◽  
Fuzzy Neural
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