Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory

2012 ◽  
Vol 25 (8) ◽  
pp. 1567-1576 ◽  
Author(s):  
Chin Wang Lou ◽  
Ming Chui Dong
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Load Forecasting ◽  
Data Uncertainty ◽  
Electric Load ◽  
Modeling Data ◽  
Electric Load Forecasting

scholarly journals Intelligent Fuzzy Logic - Cuckoo Search Algorithm Method for Short-Term Electric Load Forecasting in 150 kV Sulselrabar System

2017 ◽  
pp. 154 ◽  
Author(s):  
Muhammad Ruswandi Djalal ◽  
Faisal Faisal
Keyword(s):  
Fuzzy Logic ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Load Flow ◽  
Time Range ◽  
Electric Load ◽  
Short Term ◽  
Electric Load Forecasting

Forecasting the electrical load becomes important, because it can estimate electricity consumption over a certain time range. Accuracy in electric load forecasting can improve safety and reliability in the operation of power systems such as load flow, maintenance of generating units and scheduling of generating units. In this study used case study system Sulselrabar, which is currently growing, but still not much to discuss about the condition of the current system and which will come. Several methods for predicting electrical loads have been widely used, ranging from conventional to smart-based methods. In this research will be proposed method of artificial intelligence for forecasting Short Term load on Sulselrabar system. The method used is based Fuzzy Logic and Cuckoo Search Algorithm. The combination of Fuzzy logic and Cuckoo Search methods is chosen because the combination of both optimizes optimum fuzzy logic membership, so the forecasting results have a very small error. From the results of the research can be concluded that the result of load forecasting using Fuzzy Logic method optimized using Cuckoo Search Algorithm (FL-CSA) is better than Fuzzy Logic that is not optimized. The analysis results using input data 3 months before day H, to predict the load for one week on January 1 to 7 january 2014, and as a comparison used the predicted day H data. From the simulation results, the mean absolute percentage error (MAPE) is smaller using FLCSA, for the smallest MAPE on 1 January 2014 of 0.06785208%. While the highest MAPE on January 4, 2014 amounted to -0.44973%.

A new interval prediction methodology for short-term electric load forecasting based on pattern recognition

2021 ◽  
Vol 297 ◽  
pp. 117173
Author(s):  
Xavier Serrano-Guerrero ◽  
Marco Briceño-León ◽  
Jean-Michel Clairand ◽  
Guillermo Escrivá-Escrivá
Keyword(s):  
Pattern Recognition ◽  
Load Forecasting ◽  
Electric Load ◽  
Short Term ◽  
Interval Prediction ◽  
Electric Load Forecasting
Sign in / Sign up

Export Citation Format

Share Document