Short-term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

2005 ◽  
Vol 20 (6) ◽  
pp. 591-605 ◽  
Author(s):  
Nicolaos B. Karayiannis ◽  
Mahesh Balasubramanian ◽  
Heidar A. Malki
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Electric Power ◽  
Basis Function ◽  
Experimental Evaluation ◽  
Load Forecasting ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load

Short-term electric power load forecasting using feedforward neural networks

2004 ◽  
Vol 21 (3) ◽  
pp. 157-167 ◽  
Author(s):  
Heidar A. Malki ◽  
Nicolaos B. Karayiannis ◽  
Mahesh Balasubramanian
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Load Forecasting ◽  
Feedforward Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load

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.

Short-term electric power load forecasting using random forest and gated recurrent unit

2021 ◽  
Author(s):  
Venkataramana Veeramsetty ◽  
K. Rajeshwar Reddy ◽  
M. Santhosh ◽  
Arjun Mohnot ◽  
Gaurav Singal
Keyword(s):  
Random Forest ◽  
Electric Power ◽  
Load Forecasting ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load ◽  
Gated Recurrent Unit

Mean shift densification of scarce data sets in short-term electric power load forecasting for special days

2016 ◽  
Vol 99 (3) ◽  
pp. 881-898 ◽  
Author(s):  
Liviane Rego ◽  
Jean Sumaili ◽  
Vladimiro Miranda ◽  
Carlos Francês ◽  
Marcelino Silva ◽  
...  
Keyword(s):  
Electric Power ◽  
Mean Shift ◽  
Load Forecasting ◽  
Data Sets ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load ◽  
Scarce Data
Sign in / Sign up

Export Citation Format

Share Document