Title :
One hour ahead load forecast of PJM electricity market & UPPCL
Author :
Sahay, Kishan Bhushan ; Rana, Vishesh
Author_Institution :
Dept. of Electr. Eng., Madan Mohan Malaviya Univ. of Technol., Gorakhpur, India
Abstract :
Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.
Keywords :
artificial intelligence; load forecasting; maintenance engineering; neural nets; power generation dispatch; power generation scheduling; power markets; power system planning; power system reliability; AI; ANN model; PJM electricity market; STLF; UPPCL; Uttar Pradesh power corporation India ltd; artificial intelligence; artificial neural network; dispatch scheduling; maintenance planning; one hour-ahead load forecast; power system planning; reliability analysis; short-term load forecasting; Artificial neural networks; Biological neural networks; Electricity supply industry; Error analysis; Load forecasting; Load modeling; Neurons; Mean absolute error (MAE); Uttar Pradesh Power Corporation India Ltd. (UPPCL); mean absolute percentage error (MAPE); neural network (NN); power system; short-term load forecasting;
Conference_Titel :
Energy Economics and Environment (ICEEE), 2015 International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4673-7491-0
DOI :
10.1109/EnergyEconomics.2015.7235068