scholarly journals Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1617
Author(s):  
Kang Qian ◽  
Xinyi Wang ◽  
Yue Yuan
Keyword(s):  
Meteorological Factors ◽  
Load Forecasting ◽  
Weighted Average ◽  
Support Vector ◽  
Forecasting Model ◽  
Electric Load ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Load Forecasting

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

scholarly journals A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1520 ◽  
Author(s):  
Liu ◽  
Jin ◽  
Gao
Keyword(s):  
Parameter Optimization ◽  
Hybrid Method ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Data Preprocessing ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Whale Optimization ◽  
Electric Load Forecasting

Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids.

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