Optimal Support Vector Machine Based Short–Term Load Forecasting Model with Input Variables and Samples Selection

Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.

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Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Energies ◽

10.3390/en9100827 ◽

2016 ◽

Vol 9 (10) ◽

pp. 827 ◽

Cited By ~ 21

Author(s):

Yi Liang ◽

Dongxiao Niu ◽

Minquan Ye ◽

Wei-Chiang Hong

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Least Squares ◽

Load Forecasting ◽

Cuckoo Search ◽

Support Vector ◽

Short Term ◽

Short Term Load Forecasting

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Power Short-Term Load Forecasting Based on QPSO-SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.591-593.1311 ◽

2012 ◽

Vol 591-593 ◽

pp. 1311-1314

Author(s):

Xing Tong Zhu ◽

Bo Xu

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Quantum Particle ◽

Close Contact ◽

Load Forecasting ◽

Particle Swarm ◽

Support Vector ◽

Short Term ◽

Swarm Optimization ◽

Short Term Load Forecasting

The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally, the support vector machine was used for power short-term load forecasting. In the proposed method such factors impacting loads as meteorology,weather and date types are comprehensively considered. The experimental results show that the root-mean-square relative error of the proposed method is only 1.90%, which is less than those of SVM and PSO-SVM model by 2.29% and 2.80%, respectively.

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A short-term load forecasting approach based on support vector machine with adaptive particle swarm optimization algorithm

2009 Chinese Control and Decision Conference ◽

10.1109/ccdc.2009.5192275 ◽

2009 ◽

Cited By ~ 2

Author(s):

Huang Yue ◽

Li Dan ◽

Gao Liqun ◽

Wang Hongyuan

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Short Term Load Forecasting

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Short-Term Load Forecasting Based on Self-organizing Map and Support Vector Machine

Lecture Notes in Computer Science - Advances in Natural Computation ◽

10.1007/11539087_89 ◽

2005 ◽

pp. 688-691 ◽

Cited By ~ 3

Author(s):

Zhejing Bao ◽

Daoying Pi ◽

Youxian Sun

Keyword(s):

Support Vector Machine ◽

Load Forecasting ◽

Support Vector ◽

Self Organizing Map ◽

Short Term ◽

Short Term Load Forecasting ◽

Self Organizing

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Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Sustainability ◽

10.3390/su12177076 ◽

2020 ◽

Vol 12 (17) ◽

pp. 7076 ◽

Cited By ~ 3

Author(s):

Arash Moradzadeh ◽

Sahar Zakeri ◽

Maryam Shoaran ◽

Behnam Mohammadi-Ivatloo ◽

Fazel Mohammadi

Keyword(s):

Support Vector Regression ◽

Performance Metrics ◽

Short Term Memory ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Short Term Load Forecasting ◽

Long Short Term Memory ◽

Input Variables

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

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