AN INTELLIGENT AND HYBRID PSO WITH NEURAL NETWORK BASED SHORT TERM LOAD FORECAST MODEL

Day to day operations of power system and efficient energy management system is
very crucial to reduce the operational expenditure and electricity price. Such type of
power system planning can be carried out on the basis of accurate load forecasting.
Conventional power plants such as thermal generating units utilize the coal and fossil
fuels to generate the electricity which significantly contributes as environmental
pollution in terms of CO2 emission in the environment. The environmental pollution
can be reduced with accurate load forecasting that is one of the biggest challenges of
21s century. Moreover, overestimation and underestimation of power demand can be
avoided by utilizing the accurate load forecast model. The overestimation of load
demand may increase the power production cost and increase the unexpected
surpluses of power system. In case of underestimation of load demand, it is also
difficult to manage the overload condition for power system when large back power
storage is not available. Therefore, an accurate load forecasting can play vital role to
achieve the higher power system quality and reliability.