Daily Load Forecasting Using Support Vector Machine and Case-Based Reasoning

Database classification suffers from two common problems, i.e., the high dimensionality and nonstationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a Support Vector Machine (SVM), and Genetic Algorithms to construct a decision-making system for data classification in various database applications. The model is mainly based on the concept that the historic database can be transformed into a smaller case-base together with a group of SVM models. As a result, the model can more accurately respond to the current data under classifying from the inductions by these SVM models generated from these smaller case bases. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.

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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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Short-term power load forecasting based on support vector machine and particle swarm optimization

Journal of Algorithms & Computational Technology ◽

10.1177/1748301818797061 ◽

2018 ◽

Vol 13 ◽

pp. 174830181879706 ◽

Cited By ~ 3

Author(s):

Song Qiang ◽

Yang Pu

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Influencing Factors ◽

Load Forecasting ◽

Particle Swarm ◽

Forecast Accuracy ◽

Support Vector ◽

Model Parameters ◽

Short Term ◽

Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

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