An Intelligent System For Forecasting Electric Load And Price

Abstract—Electric load forecasting is an important research field to increase reliability of power supply and provide optimal load and minimum price scheduling in an intelligent manner. This paper presents the development of advanced neural networks to face successfully the problem of the short term Electric load forecasting. Artificial neural network (ANN) has been used for many years in various sectors and disciplines like medical, robotics, electronics, meteorology, economy, forecasts, etc. This report present the development of an Adaptive Neuro Fuzzy Inference System (ANFIS) Based short-term load forecasting model which forecast the electric load. It predict electric load by considering these information as input like date, time, humidity and previous data sets taken from the various power corporation. This model is derived by modifying the method of forecasting technique using Adaptive Neuro Fuzzy Inference System (ANFIS) which is the process of formulating the mapping from a given input to an output using fuzzy logic Fuzzy systems are expert decision making tools that require support from Artificial Neural Network (ANN) for the generation of inference. This leads to the formation of Neuro-Fuzzy Inference system (NFIS) and ANFIS are a class of adaptive networks that are functionally equivalent to fuzzy inference systems. Experimental results show that such system is effective in controlling, managing, planning and organizing the electric load by forecasting the load