Daily peak load forecasting using ANN

Author

Tasre, Mohan B. ; Bedekar, Prashant P. ; Ghate, Vilas N.

fYear

2011

fDate

8-10 Dec. 2011

Firstpage

1

Lastpage

6

Abstract

Accurate load forecasting plays a key role in economical use of energy. Artificial Neural Network (ANN) models have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. In this paper daily peak load forecasting has been performed for the part of a town supplied by 19 distribution feeders on weekdays by taking into consideration the historical maximum load (Lmax) and maximum temperature (Tmax) data. Back-Propagation algorithm is verified for Momentum learning rule (MLR) and Delta-Bar-Delta learning rule (D-B-DLR). Optimization of the network parameters is performed for both learning rules. The optimized network performances are compared in terms of the mean absolute percentage error (MAPE) and the network complexity.

Keywords

backpropagation; load forecasting; neural nets; power distribution economics; power engineering computing; ANN model; D-B-DLR; Lmax data; MAPE; MLR; Tmax data; artificial neural network model; back-propagation algorithm; daily peak load forecasting; delta-bar-delta learning rule; distribution feeder; energy economical usage; maximum load data; maximum temperature data; mean absolute percentage error; momentum learning rule; network complexity; network parameter optimization; short-term load forecasting; Artificial neural networks; Load forecasting; Load modeling; Neurons; Predictive models; Temperature distribution; Training; Artificial Neural Network; Back Propogation algorithm; Delta-Bar-Delta learning rule; Load Forecasting; Momentum learning rule;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering (NUiCONE), 2011 Nirma University International Conference on

Conference_Location

Ahmedabad, Gujarat

Print_ISBN

978-1-4577-2169-4

Type

conf

DOI

10.1109/NUiConE.2011.6153291

Filename

6153291