Paper Details

ABSTRACT

Electrical load forecasting is an essential tool used to ensure that energy supplied by utilities meet the load plus the energy lost in the system. So, to generate reasonably the required power, one needs to forecast the future electricity demands since power generation relies heavily on the electricity demand. Load forecast has three different types: short term forecast, medium term forecast and long term forecast. Since in power system the next day’s power generation must be scheduled every day, day- ahead STLF is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. This article presents the development of Adaptive Neuro Fuzzy Interface System (ANFIS) based short-term load forecasting model. The fusion of neural networks and fuzzy logic in neuro-fuzzy models achieves readability and learning ability at once. This article presents prediction of electric load by considering various information like time, temperature, humidity, wind speed, day and historical load data. Historical load data is taken from MGVCL and weather data is taken from the website www.timeanddate.com