Title :
Classification of electricity load forecasting based on the factors influencing the load consumption and methods used: An-overview
Author :
M. Mustapha;M. W. Mustafa;S. N Khalid;I. Abubakar;H. Shareef
Author_Institution :
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Abstract :
Electrical energy consumption is affected by many parameters. These includes the variables related to power system itself, weather and climatic factors and socio-economic being of the energy consumers. In this paper, two components of load forecasting are classified. The parameters that influence the energy consumption and the methods used to forecast the energy consumption are reviewed. It is observed that, many factors have great influence on the energy consumption, and the forecasting accuracy depends on the amount of data used. Also the methods applied contribute in the forecasting accuracy and complexity of the method. It is therefore important to use large data, and apply an appropriate method (technique) while forecasting electrical energy. A lot of methods are reviewed, from time series method to artificial intelligence with varying parameters, most of which are weather related, demography of the area, economy class of the consumers and the history of electrical energy consumed.
Keywords :
"Load forecasting","Biological system modeling","Load modeling","Forecasting","Meteorology","Artificial neural networks","Predictive models"
Conference_Titel :
Energy Conversion (CENCON), 2015 IEEE Conference on
DOI :
10.1109/CENCON.2015.7409585