Short-Term Load Forecasting Using Artificial Neural Network and Time Series Model to Predict the Load Demand for Delhi and Greater Noida Cities

2021 ◽  
pp. 443-455
Author(s):  
Nikita Singh ◽  
Pratyush Sharma ◽  
Nikhil Kumar ◽  
Mini Sreejeth
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Time Series Model ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

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

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.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

Artificial Neural Network Modelling for Short Term Load Forecasting

2021 ◽  
Author(s):  
Mahshooq Abdul Majeed ◽  
Soumya Mudgal ◽  
Lalit Tak ◽  
Janavi Popat ◽  
Harsh Kakadiya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Short Term Load Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Modelling
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