Electricity load dynamics, temperature and seasonality Nexus in Algeria

被引:18
作者
Chabouni, Naima [1 ]
Belarbi, Yacine [2 ]
Benhassine, Wassim [3 ]
机构
[1] Ecole Natl Super Stat & Econ Appl, Algiers, Algeria
[2] Res Ctr Appl Econ Dev CREAD, Algiers, Algeria
[3] Ecole Natl Polytech, Algiers, Algeria
关键词
Electricity load; Temperature; Seasonality; Dynamic effects; Autoregressive effects; SUPPORT VECTOR REGRESSION; CLIMATE-CHANGE; KALMAN FILTER; DEMAND; CONSUMPTION; MODELS; ARIMA; TIME; IMPACTS; SYSTEMS;
D O I
10.1016/j.energy.2020.117513
中图分类号
O414.1 [热力学];
学科分类号
摘要
During the last decade, major changes have affected the electricity sector in Algeria. As consequence it recorded an important increase of electricity demand in energy and capacity under the mixt pressure of demography and socio-economic development, climate change, and the depletion of natural gas reserves which imposes new challenges in terms of renewable energies and demand side management. These adjustments enhanced the important role of electricity demand forecasting. The aim of this study is to construct the best overall model that represents the relationship between electricity demand and air temperature, i.e. heating and cooling degree days, and taking into consideration other deterministic variables. A simple multiple regression model has been developed, since it allows us to investigate this relationship in an easy and controlled manner. Additionally, the model can be used to forecast electricity demand for the next year on a daily basis. The results show that CDD and HDD have the highest effect on electricity demand, and can be seen as the main factors affecting the daily load in Algeria. On the other hand, holidays reduce electricity demand in all seasons. We also secluded the dummy representing the holy month of Ramadan where it was clear that the behavior during this holiday increased the electricity demand especially in the summer seasons. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:14
相关论文
共 78 条
[21]   Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods [J].
de Oliveira, Erick Meira ;
Cyrino Oliveira, Fernando Luiz .
ENERGY, 2018, 144 :776-788
[22]   Electricity consumption modelling: A case of Germany [J].
Do, Linh Phuong Catherine ;
Lin, Kuan-Heng ;
Molnar, Peter .
ECONOMIC MODELLING, 2016, 55 :92-101
[23]   MODELING PEAK ELECTRICITY DEMAND [J].
ENGLE, RF ;
MUSTAFA, C ;
RICE, J .
JOURNAL OF FORECASTING, 1992, 11 (03) :241-251
[24]   Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey [J].
Erdogdu, Erkan .
ENERGY POLICY, 2007, 35 (02) :1129-1146
[25]   Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model [J].
Fan, Guo-Feng ;
Peng, Li-Ling ;
Hong, Wei-Chiang .
APPLIED ENERGY, 2018, 224 :13-33
[26]   Impacts of climate change on electricity demand in China: An empirical estimation based on panel data [J].
Fan, Jing-Li ;
Hu, Jia-Wei ;
Zhang, Xian .
ENERGY, 2019, 170 :880-888
[27]  
Feinberg EA, 2003, POWER AND ENERGY SYSTEMS, PROCEEDINGS, P88
[28]   Regression analysis for prediction of residential energy consumption [J].
Fumo, Nelson ;
Biswas, M. A. Rafe .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 47 :332-343
[29]   Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands [J].
Hekkenberg, M. ;
Benders, R. M. J. ;
Moll, H. C. ;
Uiterkamp, A. J. M. Schoot .
ENERGY POLICY, 2009, 37 (04) :1542-1551
[30]  
Hinman J., 2009, Modeling and Forecasting Short-Term Electricity Load Using Regression Analysis, P1