A New Model to Short-Term Power Load Forecasting Combining Chaotic Time Series and SVM

2009 ◽  
Author(s):  
Dongxiao Niu ◽  
Yongli Wang
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Chaotic Time Series ◽  
Short Term ◽  
New Model ◽  
Power Load ◽  
Power Load Forecasting

Short-Term Reactive Power Load Forecasting Using Multiple Time-Series Model

2003 ◽  
Vol 36 (20) ◽  
pp. 985-990 ◽  
Author(s):  
Jong-Man Cho ◽  
Jin-Hack Kim ◽  
Woo-Hyun Park ◽  
Yun-ho Lee ◽  
Jin-O Kim
Keyword(s):  
Time Series ◽  
Reactive Power ◽  
Load Forecasting ◽  
Time Series Model ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1063 ◽  
Author(s):  
Horng-Lin Shieh ◽  
Fu-Hsien Chen
Keyword(s):  
Energy Efficiency ◽  
Electric Power ◽  
Production Efficiency ◽  
Load Forecasting ◽  
Short Term ◽  
Inference System ◽  
Power Load ◽  
Power Load Forecasting ◽  
New Energy ◽  
Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

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