Investigation of Performance of Electric Load Power Forecasting in Multiple Time Horizons With New Architecture Realized in Multivariate Linear Regression and Feed-Forward Neural Network Techniques
- 14 July 2020
- journal article
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industry Applications
- Vol. 56 (5) , 5603-5612
Abstract
A new multiple parallel inputs and parallel outputs architecture-based models are developed for forecasting of electric load power consumption. Attention was paid towards the improvement of the accuracy of forecasting using this new architecture built-in by us as compared to others in the latest literatures. The models have an ability to forecast daily load profiles with a lead time of one to seven days. Both Multivariate Linear Regression (MvLR) and Feed-forward Neural Network (FFNN) techniques have been chosen for comparative performance study and analysis. The real time data used for this research work were collected from Tata Power Delhi Distribution Limited (TPDDL), Delhi, India. Based on the performance criteria provided in literatures, each model is analyzed and the results are presented for two different lead times i.e. day-ahead and week-ahead only.Keywords
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