Daily Peak Load Forecast Using Artificial Neural Network

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

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Short-Term Load Forecasting for Peak Load Reduction Using Artificial Neural Network Technique

Advances in Computer, Communication and Control - Lecture Notes in Networks and Systems ◽

10.1007/978-981-13-3122-0_56 ◽

2019 ◽

pp. 551-559 ◽

Cited By ~ 1

Author(s):

Ayandeep Ganguly ◽

Kuheli Goswami ◽

Arpita Mukherjee ◽

Arindam Kumar Sil

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Peak Load ◽

Load Forecasting ◽

Load Reduction ◽

Short Term ◽

Short Term Load Forecasting ◽

Artificial Neural

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Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.01.002 ◽

2021 ◽

Vol 2 (1) ◽

pp. 1-22

Author(s):

Kumilachew Chane ◽

◽

Fsaha Mebrahtu Gebru ◽

Baseem Khan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Electric Utility ◽

Percentage Error ◽

Electrical Load ◽

Short Term ◽

Load Demand ◽

Short Term Load Forecasting ◽

Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

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