TY - GEN
T1 - Classification of electricity load forecasting based on the factors influencing the load consumption and methods used
T2 - 2nd IEEE Conference on Energy Conversion, CENCON 2015
AU - Mustapha, M.
AU - Mustafa, M. W.
AU - Khalid, S. N.
AU - Abubakar, I.
AU - Shareef, H.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Classification
KW - Load consumption
KW - Load forecasting methods
KW - load factors
UR - http://www.scopus.com/inward/record.url?scp=84964370044&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964370044&partnerID=8YFLogxK
U2 - 10.1109/CENCON.2015.7409585
DO - 10.1109/CENCON.2015.7409585
M3 - Conference contribution
AN - SCOPUS:84964370044
T3 - 2015 IEEE Conference on Energy Conversion, CENCON 2015
SP - 442
EP - 447
BT - 2015 IEEE Conference on Energy Conversion, CENCON 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 October 2015 through 20 October 2015
ER -