scholarly journals Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1613 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Knowledge Representation ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Learning Machine ◽  
Electricity Load Forecasting

scholarly journals Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2907 ◽  
Author(s):  
Waqas Ahmad ◽  
Nasir Ayub ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Muhammad Awais ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Support Vector ◽  
Grid Search ◽  
Electricity Load ◽  
Learning Machine ◽  
Improved Support Vector Machine ◽  
Electricity Load Forecasting

Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.

Analysis of temperature-sensitive on short-term electricity load forecasting

2020 ◽  
Author(s):  
Nguyen Xuan Tung ◽  
Nguyen Quang Dat ◽  
Tran Ngoc Thang ◽  
Vijender Kumar Solanki ◽  
Nguyen Thi Ngoc Anh
Keyword(s):  
Load Forecasting ◽  
Temperature Sensitive ◽  
Short Term ◽  
Electricity Load ◽  
Electricity Load Forecasting

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.

scholarly journals ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting
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