Short-term load forecasting based on improved extreme learning machine

2017 ◽  
Author(s):  
Jie Li ◽  
Zhongyou Song ◽  
Yuanhong Zhong ◽  
Zhaoyuan Zhang ◽  
Jianhong Li
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Learning Machine

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.

A switching delayed PSO optimized extreme learning machine for short-term load forecasting

2017 ◽  
Vol 240 ◽  
pp. 175-182 ◽  
Author(s):  
Nianyin Zeng ◽  
Hong Zhang ◽  
Weibo Liu ◽  
Jinling Liang ◽  
Fuad E. Alsaadi
Keyword(s):  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Learning Machine

scholarly journals Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

2019 ◽  
Vol 9 (20) ◽  
pp. 4215 ◽  
Author(s):  
Zhengmin Kong ◽  
Zhou Xia ◽  
Yande Cui ◽  
He Lv
Keyword(s):  
Correlation Analysis ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Integration Scheme ◽  
Electric Load ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods.

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