Am. Sharaf et Tt. Lie, A NOVEL NEURO-FUZZY BASED SELF-CORRECTING ONLINE ELECTRIC-LOAD FORECASTING-MODEL, Electric power systems research, 34(2), 1995, pp. 121-125
The paper presents a neuro-fuzzy short-term load forecasting (STLF) mo
del. The proposed ANN function approximator models the relationships b
etween the system hourly peak load and system variables affecting it,
namely, weather and temperature variations, type and time of day, the
inherent parameters of historical load patterns such as trend, cyclic
oscillations, regular seasonal and irregular 'special' events. The loa
d predictor forecasting input vector was extended to account for most
of the input dominant variables affecting the short-term forecast load
. The model utilizes a preprocessor for input vector generation and pr
iority classifications using historical load and system data. A postpr
ocessor fuzzy logic block provides error correction and data filtering
and online tuning and adjustment of electric load forecast data.