TY  - JOUR
AV  - none
TI  - Neural network based STLF model to study the seasonal impact of weather and exogenous variables
SP  - 3729
N1  - cited By 19
SN  - 20407459
PB  - Maxwell Science Publications
EP  - 3735
ID  - scholars3941
N2  - Load forecasting is very essential for efficient and reliable operation of the power system. Uncertainties of weather behavior significantly affect the prediction accuracy, which increases the operational cost. In this study, neural network (NN) based 168 hours ahead short term load forecast (STLF) model is proposed to study seasonal impact of calendar year. The affect of the model inputs such as, weather variables, calendar events and type of a day on load demand is considered to enhance the forecast accuracy. The weight update equations of gradient descent algorithm are derived and Mean Absolute Percentage Error (MAPE) is used as performance index. The performance of NN is measured in terms of confidence interval, which is based on training, testing, validation and cumulative impact of these phases. The simulations result shows that the forecast accuracy is affected by seasonal variation of input data. © Maxwell Scientific Organization, 2013.
IS  - 20
Y1  - 2013///
VL  - 6
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884308138&doi=10.19026%2frjaset.6.3583&partnerID=40&md5=57d818bc6ecacfafb7d72b89b43f7d50
A1  - Raza, M.Q.
A1  - Baharudin, Z.
A1  - Badar-Ul-Islam
A1  - Azman Zakariya, M.
A1  - Khir, M.H.M.
JF  - Research Journal of Applied Sciences, Engineering and Technology
ER  -