Short-term load forecasting of Gothatar feeder of Nepal Electricity Authority using Recurrent Neural Network
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Authors
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Publisher
IOE Pulchowk Campus
Abstract
This paper mainly focuses on short-term forecasting, gives an hourly demand forecast of
electricity. Forecasting using Recurrent Neural Network (RNN) is helpful for making
important decisions in the field of preventing misbalancing in load and power generation,
scheduling, load switching strategies, preventing imbalance in the load demand and power
generation, thus leading to greater power quality and network reliability. We use a method
called Recurrent Neural Network (RNN) to anticipate the future hourly demand of Gothatar
feeder, Nepal Electricity Authority (NEA). The RNN Network is build, trained and test
with historical hourly demand data along with six different input variables and used for the
prediction of day ahead hourly demand. The output from RNN model is validated with the
real hourly demand data collected from NEA. In addition, the load forecasting is performed
for short term load forecasting (STLF) using some other time series methods like: Single
Exponential Smoothing (SES), Double Exponential Smoothing (DES) and Holt-Winter's
method as well, and whose output was compared with that of RNN. The Root Mean Square
Error (RMSE) of SES, DES and Holt-Winter's method was found to be 188.033 kVA,
181.066 kVA and 169.759 kVA respectively, degree of Determination (R
2
) was 0.609,
0.618 and 0.634 and Mean Average Percentage Error (MAPE) was found to be 15.421%,
13.31% and 11.502% respectively. The RNN method proved to be the accurate and best
forecasting method when the results are compared with other forecasting methods in terms
of different error measurements i.e., RMSE, R
2
and MAPE. Root Mean Square Error
(RMSE) of 69.03 kVA, R
2
of 0.876 and MAPE of 4.35% obtained from RNN. So, the RNN
model proved to be the most accurate and best method with very less error and better R2
in
this study
Description
In last few years, the electrical short-term load forecasting is evolving as one of the most
important field of research for the reliable and efficient power operation. It plays crucial
role in the field of scheduling, load flow analysis, contingency analysis, maintenance and
planning of power system