Hybrid artificial neural network system for short-term load forecasting

Short-Term Load Forecasting Using Artificial Neural Network

-Artificial neural network (ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its application to forecasting problems. This report present the development of an ANN based short-term load forecasting model for the 132...

Short-term Electrical Load Forecasting for an Institutional/Industrial Power System Using an Artificial Neural Network

Artificial Neural Network Based Approach for short load forecasting

Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short ter...

Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

Artificial Neural Network and ANFIS Based Short Term Load Forecasting in Real Time Electrical Load Environment

An efficient and accurate electrical power Short Term Load forecasting plays a vital role for economic operational planning of both the electricity markets as well as regulated power systems. Till date many techniques and approaches have been presented for STLF in the literature. However there is still an essential need to develop more efficient and accurate load forecast model. This paper uses...