No abstract

Increasing levels of climatic warming are expected to affect the global development of energy consumption. The cooling degree day (CDD) is one of the climate-driven indices that captures the impact of climate on energy demand. However, little is known about the spatiotemporal trends of CDD in relation to a changing climate and economy in West Africa and its main implications. Hence, in order to analyze how energy demand could evolve, this study aims to assess the changes in CDD under 1.5, 2.0, 2.5, and 3.0 °C global warming levels (GWLs), with and without population exposure and trends under the two representative concentration pathways (RCPs) of RCP4.5 and RCP8.5 for West Africa. A climate-reflective base temperature (T-base) is used and was determined using a piecewise linear regression method. Seasonal electricity consumption was derived using a decomposition feature. An ensemble of seven Global Climate Models (GCMs) were used for the future temperature projections. The future population was based on shared socioeconomic pathway outputs. Based on the analysis, the reported average T-base for the West African region is 24 °C. An increasing CDD trend was identified in all of the RCP scenarios, but is more pronounced in RCP8.5. RCP8.5 departs from the mean historical period of approximately 20% by 2100 with the standardized value. The same trend is observed under different GWLs as the warming level increased and was most striking in the Sahelian zone. Population exposure to CDD (labelled CDDP) increases with warming levels, but is more pronounced in highly agglomerated areas. The CDDP index best captures the spatial representation of areas with high cooling demand potential with respect to the demographic distribution. This study can serve to inform better energy demand assessment scenarios and supply planning against the backdrop of changing climate conditions in West Africa.

This data article is related to the research article “O.D.T. Odou, R. Bhandari, R. Adamou, Hybrid off-grid renewable power system for sustainable rural electrification in Benin, Renew. Energy. 145 (2020) 1266–1279. doi:10.1016/j.renene.2019.06.032.’’. The data presented are grouped into four (04) groups as follows: Load, Ressources, Components costs and specification and Optimization and Simulation data. The data are mainly acquired from onsite survey for the load demand, National Direction of Water (DGeau) for rivers streamflow, National Direction of Meteorology (DNM) for meteorological data, expert knowledge and HOMER software model output data. An empirical method is used to estimate the river streamflow at Fouay from the known gauged streamflow data. The purpose of this article is to make available reliable open access data to allow replicability and enhance research in similar studies while giving first-hand information to users.