Risk due to load forecast uncertainty in short term power system planning

1998 ◽  
Vol 13 (4) ◽  
pp. 1493-1499 ◽  
Author(s):  
A.P. Douglas ◽  
A.M. Breipohl ◽  
F.N. Lee ◽  
R. Adapa
Keyword(s):  
Power System ◽  
Power System Planning ◽  
Short Term ◽  
System Planning ◽  
Forecast Uncertainty ◽  
Load Forecast

Flexibility Metric for Power System Planning Under Forecast Uncertainty

2021 ◽  
Author(s):  
Akila Herath ◽  
Kithsiri M. Liyanage ◽  
M.A. Mohammed Manaz ◽  
Taisuke Masuta ◽  
Chan-Nan Lu
Keyword(s):  
Power System ◽  
Power System Planning ◽  
System Planning ◽  
Forecast Uncertainty

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

2010 ◽  
Author(s):  
R. M. Holmukhe ◽  
Mrs. Sunita Dhumale ◽  
Mr. P. S. Chaudhari ◽  
Mr. P. P. Kulkarni ◽  
Swapan Paruya ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Load Forecasting ◽  
Power System Planning ◽  
Short Term ◽  
Fuzzy Logic Systems ◽  
System Planning ◽  
Short Term Load Forecasting ◽  
Logic Systems

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.

scholarly journals Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

2021 ◽  
Author(s):  
Karl‐Kiên Cao ◽  
Jannik Haas ◽  
Evelyn Sperber ◽  
Shima Sasanpour ◽  
Seyedfarzad Sarfarazi ◽  
...  
Keyword(s):  
Power System ◽  
Large Scale ◽  
Energy Systems ◽  
Power System Planning ◽  
System Planning

Simulations of wind power integration with complementary power system planning tools

2008 ◽  
Vol 78 (6) ◽  
pp. 1069-1079 ◽  
Author(s):  
Poul Sørensen ◽  
Ian Norheim ◽  
Peter Meibom ◽  
Kjetil Uhlen
Keyword(s):  
Power System ◽  
Wind Power ◽  
Power System Planning ◽  
System Planning ◽  
Wind Power Integration ◽  
Planning Tools
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