Neural network based prediction for optimum power system operation

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.