Short term load forecasting using neural networks

• Main Authors: Nigrini, L.B., Jordaan, G.D.
• Other Authors: Central University of Technology, Free State, Bloemfontein
• Format: Others
• Language: en_US
• Published: Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein 2015
• Subjects: Short Term Load Forecasting (STLF); Artificial Neural Network (ANN); Time series; Multilayer feed forward network; Real time load data
• Online Access: http://hdl.handle.net/11462/646

Published Article === Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point. In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.