Modelling of unsuppressed electrical demand forecasting in Iraq for long term

被引:16
作者
Mohammed, Nooriya A. [1 ,2 ]
机构
[1] Univ Erlangen Nurnberg, Nurnberg, Germany
[2] Minist Elect, Planning & Studies Off, Baghdad, Iraq
关键词
Load forecast; Electricity demand; Suppression demand;
D O I
10.1016/j.energy.2018.08.030
中图分类号
O414.1 [热力学];
学科分类号
摘要
One of the main obstacles to Iraq's economic and social development is the lack of reliable electricity supply. In recent years there has been a significant increase in grid-based electricity capacity, but it is still far from being sufficient to meet demand growth, Therefore, it is necessary to build a suitable and flexible forecasting model for this energy system. In this paper, the results of two models are compared to other previous studies of Iraq's energy system to provide the yearly unsuppressed load forecast in the long term. The relationship between the actual load supply and four sets of historical data: population, gross national product, consumer price index and temperature, is examined, The result shows that a reduction in the prediction of electricity demand including suppressed demand occurs when increasing the growth of the consumer price index and removing the war affect. The suppressed consumer demand is estimated by developing a heuristic algorithm and the impact of the reserve margin and load diversity factor is considered to obtain the final forecast. The main contribution of this paper integrates various factors, after rebuilding the lost information, and includes the influence of relevant independent variables, each one for a given weight. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:354 / 363
页数:10
相关论文
共 21 条
[1]  
Agency I. E., 2012, IRAQ EN OUTL
[2]  
Angelopoulos D, 2017, 2017 IEEE MANCHESTER POWERTECH
[3]  
[Anonymous], 2006, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
[4]   Electricity consumption forecasting in Italy using linear regression models [J].
Bianco, Vincenzo ;
Manca, Oronzio ;
Nardini, Sergio .
ENERGY, 2009, 34 (09) :1413-1421
[5]  
Charles RH, 2002, HIST IRAQ
[6]  
Franco M., 2006, 2006 IEEE/PES Transmission & Distribution Conference & Exposition: Latin America (IEEE Cat. No. 06EX1340)
[7]  
Ghods L, 2010, IRAN J ELECT ELECT E, V6
[8]  
Himanshu AA, 2007, ENERGY, V33, P724
[9]  
HONG W.-C., 2013, Intelligent energy demand forecasting
[10]  
Ismail Z., 2009, American Journal of Applied Sciences, V6, P1618, DOI 10.3844/ajassp.2009.1618.1625