Research article

Effect of weather on monthly electricity consumption in three coastal cities in West Africa

  • Received: 06 December 2020 Accepted: 24 March 2021 Published: 09 April 2021
  • In several regions worldwide, demand for electricity can be highly dependent on weather conditions. This study investigates the relationships between weather and electricity consumption in three West African cities. Monthly electricity consumption datasets for the cities of Abidjan (Ivory Coast), Cotonou (Benin) and Lomé (Togo) for the 1990–2015, 2000–2015 and 2008–2014 periods respectively were collected from national electricity companies, and meteorological data of the synoptic stations were used to compute Cooling Degree-Days in the three cities. The Cooling Degree-Days indices were estimated using air temperature and two temperature indices (the Humidex and the Heat Index). For the statistical analysis, classical multiplicative decomposition was applied to consumption data for subperiods for which consumption was considered to show relatively homogeneous evolutionary behavior (Abidjan and Lomé from 2011 to 2014 and Cotonou from 2009 to 2014). Regardless of the temperature indices considered in the three cities, the Cooling Degree-Days indices are well correlated with the seasonal variability of power consumption and particularly, the peak consumption observed in March and the lower consumption in August. Slightly better correlations are obtained for Cotonou and Abidjan when the heat index (combining both temperature and relative humidity) are used to calculate the Cooling Degree-Days.

    Citation: Ghafi Kondi Akara, Benoit Hingray, Adama Diawara, Arona Diedhiou. Effect of weather on monthly electricity consumption in three coastal cities in West Africa[J]. AIMS Energy, 2021, 9(3): 446-464. doi: 10.3934/energy.2021022

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  • In several regions worldwide, demand for electricity can be highly dependent on weather conditions. This study investigates the relationships between weather and electricity consumption in three West African cities. Monthly electricity consumption datasets for the cities of Abidjan (Ivory Coast), Cotonou (Benin) and Lomé (Togo) for the 1990–2015, 2000–2015 and 2008–2014 periods respectively were collected from national electricity companies, and meteorological data of the synoptic stations were used to compute Cooling Degree-Days in the three cities. The Cooling Degree-Days indices were estimated using air temperature and two temperature indices (the Humidex and the Heat Index). For the statistical analysis, classical multiplicative decomposition was applied to consumption data for subperiods for which consumption was considered to show relatively homogeneous evolutionary behavior (Abidjan and Lomé from 2011 to 2014 and Cotonou from 2009 to 2014). Regardless of the temperature indices considered in the three cities, the Cooling Degree-Days indices are well correlated with the seasonal variability of power consumption and particularly, the peak consumption observed in March and the lower consumption in August. Slightly better correlations are obtained for Cotonou and Abidjan when the heat index (combining both temperature and relative humidity) are used to calculate the Cooling Degree-Days.



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    [1] IRENA (2012) Prospects for the African Power Sector; 60.
    [2] Pineau PO (2008) Electricity sector integration in West Africa. Energy Policy 36: 210-223. doi: 10.1016/j.enpol.2007.09.002
    [3] REN21 (2014) ECOWAS Renewable Energy and Energy Efficiency Status Report; 86.
    [4] Karekezi S, Kithyoma W (2002) Renewable energy strategies for rural Africa: is a PV-led renewable energy strategy the right approach for providing modern energy to the rural poor of sub-Saharan Africa? Energy Policy 30: 1071-1086.
    [5] ECREEE (2015) Politique d'Efficacité Énergétique de la CEDEAO; 74.
    [6] World Bank Group (2021) Access to electricity (% of population). Available from: https://data.worldbank.org.
    [7] Rojey A (2011) Comment assurer la transition énergétique; 37.
    [8] REN21 (2016) Renewables 2016 Global Status Report; 272.
    [9] REN21 (2015) Renewables 2015 Global Status Report; 251.
    [10] Schipper L, Meyers S, Howarth RB, et al. (1992) Energy efficiency and human activity: Past trends, future prospects. Cambridge; New York, NY, USA: Cambridge University Press; 400.
    [11] Akbari H (2005) Energy saving potentials and air quality benefits of urban heat island mitigation[Internet]. Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, 19.
    [12] Shahmohamadi P, Che-Ani AI, Ramly A, et al. (2010) Reducing urban heat island effects: A systematic review to achieve energy consumption balance. Int J Phys Sci, 11.
    [13] Hamlet AF, Lee S-Y, Mickelson KEB, et al. (2010) Effects of projected climate change on energy supply and demand in the Pacific Northwest and Washington State. Clim Change 102: 103-128. doi: 10.1007/s10584-010-9857-y
    [14] Aivalioti S (2015) Electricity Sector Adaptation to Heat Waves, 51.
    [15] Hernández L, Baladrón C, Aguiar JM, et al. (2012) A study of the relationship between weather variables and electric power demand inside a smart grid/smart world framework. 12: 11571-11591.
    [16] Akinlo AE (2009) Electricity consumption and economic growth in Nigeria: Evidence from cointegration and co-feature analysis. J Policy Model 31: 681-693. doi: 10.1016/j.jpolmod.2009.03.004
    [17] Bessec M, Fouquau J (2008) The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach. Energy Econ 30: 2705-2721. doi: 10.1016/j.eneco.2008.02.003
    [18] Isaac M, van Vuuren DP (2009) Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 37: 507-21. doi: 10.1016/j.enpol.2008.09.051
    [19] Franç ois B, Borga M, Creutin JD, et al. (2016) Complementarity between solar and hydro power: Sensitivity study to climate characteristics in Northern-Italy. Renewable Energy 86: 543-553. doi: 10.1016/j.renene.2015.08.044
    [20] Apadula F, Bassini A, Elli A, et al. (2012) Relationships between meteorological variables and monthly electricity demand. Appl Energy 98: 346-356. doi: 10.1016/j.apenergy.2012.03.053
    [21] Marvuglia A, Messineo A (2012) Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia 14: 45-55. doi: 10.1016/j.egypro.2011.12.895
    [22] Scapin S, Apadula F, Brunetti M, et al. (2016) High-resolution temperature fields to evaluate the response of Italian electricity demand to meteorological variables: an example of climate service for the energy sector. Theor Appl Climatol 125: 729-742. doi: 10.1007/s00704-015-1536-5
    [23] Adjamagbo C, Ngae P, Vianou A, et al. (2011) Modeling the demand for electrical energy in Togo. Rev Energy Renouvelables 14: 67-83.
    [24] Beccali M, Cellura M, Lo Brano V, et al. (2008) Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area. Renew Sustain Energy Rev 12: 2040-65. doi: 10.1016/j.rser.2007.04.010
    [25] US Department of Commerce N (2020) What is the heat index? NOAA's National Weather Service.
    [26] Bertrand REYSSET (2008) Impact of climate change on the energy sector in France, 5.
    [27] Adom PK, Bekoe W, Akoena SKK (2012) Modelling aggregate domestic electricity demand in Ghana: An autoregressive distributed lag bounds cointegration approach. Energy Policy 42: 530-537. doi: 10.1016/j.enpol.2011.12.019
    [28] Ouédraogo IM (2010) Electricity consumption and economic growth in Burkina Faso: A cointegration analysis. Energy Econ 32: 524-531. doi: 10.1016/j.eneco.2009.08.011
    [29] Inglesi-Lotz R, Blignaut JN (2011) South Africa's electricity consumption: A sectoral decomposition analysis. Appl Energy 88: 4779-4784. doi: 10.1016/j.apenergy.2011.06.018
    [30] UNECA (2016) Nineteenth (19th) session of the intergovernmental committee of experts (ice-19) of West Africa. Outcome document draft conclusions and recommendations, 8.
    [31] IEA, OECD (2014) Africa Energy Outlook : a focus on energy prospects in Sub-Saharan Africa. World Energy Outlook Special Report. Fance: International Energy Agency, 242.
    [32] IEA, OECD (2015) Energy and Climate Change, World Energy Outlook Special Report. International Energy Agency, 200.
    [33] CIA (2015) World Factbook. Electricity Production/Countries of the World.
    [34] UNOSD (2016) Seminar on Mainstreaming Energy for Sustainable Development Goals (SDGs), Targets and Indicators into Statistical Programmes in Select African Countries, 27-29 June 2016, Addis Ababa, Ethiopia.
    [35] IEA (2014) World energy outlook 2014 factsheet: Energy in sub-Saharan Africa today.
    [36] World Bank (2017) Where we work. World Bank.
    [37] Perspective monde (2021) Togo-PIB par habitant ($ US constant 2010)|Statistiques. Outil pédagogique des grandes tendances mondiales depuis 1945 École de politique appliquée Faculté des lettres et sciences humaines.
    [38] Atlas Overview (2010) Climate|West Africa, 157.
    [39] AccuWeather (2016) Global Current Weather|AccuWeather.
    [40] Cedar Lake Ventures, Inc (2016) The Typical Weather Anywhere on Earth-Weather Spark.
    [41] Commonwealth of Australia (2002) Appliance Electricity End-Use: Weather and Climate Sensitivity, 69.
    [42] Robinson PJ (2001) On the definition of a heat wave. J Appl Meteorol 40: 762-775. doi: 10.1175/1520-0450(2001)040<0762:OTDOAH>2.0.CO;2
    [43] Masterton JM, Richardson FA (1979) Humidex : a method of quantifying human discomfort due to excessive heat and humidity. Meteorological Service of Canada, 45.
    [44] Rome S, Oueslati B, Moron V, et al. (2016) Heat waves in Sahel : definition and main spatial-temporal characteristics (1973-2014). In: J.-M. Fallot DJ & NB, éditeur. 29ème Colloque de l'Association Internationale de Climatologie, 345-350.
    [45] Oueslati B, Sambou M-JG, Pohl B, et al. (2016) Sahelian heat waves: characterization, mechanisms, predictability. 29e Colloque de l'Association Internationale de Climatologie. Besanç on, France: Association Internationale de Climatologie, 327-332.
    [46] Martin Bromley (2016) Degree Days : Understanding Heating and Cooling Degree Days.
    [47] Jiang F, Li X, Wei B, et al. (2009) Observed trends of heating and cooling degree-days in Xinjiang Province, China. Theor Appl Climatol 97: 349-360. doi: 10.1007/s00704-008-0078-5
    [48] BizEE Software (2020) Degree Days—An Introduction.
    [49] Aceituno P (1979) Statistical formula to estimate heating or cooling degree-days. Agric Meteorol 20: 227-232. doi: 10.1016/0002-1571(79)90023-2
    [50] d'AMBROSIO ALFANO FR, Palella BI, Riccio G (2011) Thermal Environment Assessment Reliability Using Temperature —Humidity Indices. Ind Health 49: 95-106. doi: 10.2486/indhealth.MS1097
    [51] Adjamagbo C, Ngae P, Vianou A (2011) Modélisation de la demande en énergie électrique au Togo, 17.
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