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

• Main Author: RAZA, MUHAMMAD QAMAR
• Format: Thesis
• Language: English
• Published: 2014
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/21122/1/2014%20-%20ELECTRICAL%20-%20AN%20INTELLIGENT%20AND%20HYBRID%20PSO%20WITH%20NEURAL%20NETWORK%20BASED%20SHORT%20TERM%20LOAD%20FORECAST%20MODEL%20-%20MUHAMMAD%20QAMAR%20RAZA.pdf; http://utpedia.utp.edu.my/21122/
• Institution: Universiti Teknologi Petronas

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.