Abstract:
Neural network techniques are widely use for Load forecasting and accuracy
depends on the No. of past data, Network structure & influencing factors to
Electricity demand, such as Day of the week, Month of the year (reflect
whether, sun rise/set times - monthly cyclic patterns), Temperature, Humidity,
Wind, Public Holidays etc. Western province of Sri Lanka consumes major
part of Electricity generation, than other areas. So any whether pattern change
in other areas would not be affected to the demand pattern considerably. But
night peak this is not true.//
By examining the past load curve patterns, it is revealed that the major
influencing factors are time of the day, Day No., Month No., Public Holiday
status & School day or not others are minor factors. But however temperature
& Humidity also contribute to some extent, so these two factors also
considered. Running pattern of Mini-Hydro plants has not been monitoring by
the System control Centre, Therefore the loading pattern of those plants is not
considered. But it is understood that the running pattern depends on the rain
fall of particular area. These all plants are run of river plants, so during rainy
season almost all plants runs their full capacity (around 80MW).//
The main idea of this exercise is to develop a fairly accurate method of load
forecasting by using Neural networks and prepare an Economic dispatch
schedule at any given time, which is very useful for day to day power system
operations.//
Neural network tool box functions & graphical user interface in MATHLAB
version 6.5 is used to develop the neural network and to prepare the Machine
dispatch schedule.
