Short-term Load Forecasting Using Genetic- Fuzzy Algorithm and Neural Network

Abstract: With the growth of consumption and connected load of power systems with each other the amount of incoming and outgoing data in order to plan and optimize utilization has been Increased. So that those responsible for decisions related to these in order to support operations of power system we must use more science and software such as intelligent networks to predict Intelligent networks and new computational methods to predict the output of the power grids to provide answers. Neural networks as intelligent tools that can be used to predict but cannot be used to predict various data types in any condition, So to better predict combination of systems is used. In order to predict short-term load dispatching in Tehran Regional District we combine genetic - fuzzy algorithms with neural network method. Inputs include important data of Tehran weather conditions classified by the genetic–Fuzzy algorithm. by Using software(MATLAB ) and suggested algorithms and simulations carried out, the results are more accurate forecasts.

Keywords:

Power grids , Neural Networks , load forecasting , dispatching , Genetic-Fuzzy