Short Term Load Forecasting for Shiraz Region Using Adaptive Back Propagation Neural Network

2013 ◽  
Author(s):  
A. K. Choobeh
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Comparison of Regression and Neural Network Model for Short Term Load Forecasting: A Case Study

2021 ◽  
Vol 12 (1) ◽  
pp. 29
Author(s):  
Javaid Aslam ◽  
Waqas Latif ◽  
Muhammad Wasif ◽  
Iftikhar Hussain ◽  
Saba Javaid
Keyword(s):  
Neural Network ◽  
Power System ◽  
Research Work ◽  
Load Forecasting ◽  
Back Propagation ◽  
Electricity Consumption ◽  
Power System Planning ◽  
Short Term ◽  
System Planning ◽  
Short Term Load Forecasting

Short term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors such as time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations used for prediction, later the model improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR observed as 47% and it reduced to 8.9% for CFBP.

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