Ensemble power load forecasting based on competitive-inhibition selection strategy and deep learning

2022 ◽  
Vol 51 ◽  
pp. 101940
Author(s):  
Hua Luo ◽  
Haipeng Zhang ◽  
Jianzhou Wang
Keyword(s):  
Deep Learning ◽  
Competitive Inhibition ◽  
Load Forecasting ◽  
Selection Strategy ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14992-15003
Author(s):  
Khursheed Aurangzeb ◽  
Musaed Alhussein ◽  
Kumail Javaid ◽  
Syed Irtaza Haider
Keyword(s):  
Deep Learning ◽  
Load Forecasting ◽  
Learning Model ◽  
Similar Profile ◽  
Power Load ◽  
Power Load Forecasting ◽  
Deep Learning Model

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.

Power Load Forecasting Based on VMD and Attention-LSTM

2020 ◽  
Author(s):  
Han-Chieh Chao ◽  
Fu Lin ◽  
Jeng-Shyang Pan ◽  
Wei-Che Chien ◽  
Chin-Feng Lai
Keyword(s):  
Load Forecasting ◽  
Power Load ◽  
Power Load Forecasting
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