| dc.contributor.author | Odou, Oluwarotimi Delano Thierry | |
| dc.contributor.author | N’diaye, Aissatou | |
| dc.contributor.author | Kondi Akara, Ghafi | |
| dc.contributor.author | Sawadogo, Windmanagda | |
| dc.contributor.author | Bonkaney, Abdou Latif | |
| dc.contributor.author | Atchikpae, Tchègoun Michel | |
| dc.contributor.author | Adamou, Rabani | |
| dc.date.accessioned | 2026-08-11T14:05:43Z | |
| dc.date.available | 2026-08-11T14:05:43Z | |
| dc.date.issued | 2022-10-01 | |
| dc.identifier.uri | http://197.159.135.214/jspui/handle/123456789/1345 | |
| dc.description | A Publication submitted to the West African Science Service Centre on Climate Change and Adapted Land Use and the Université Abdou Moumouni, Niger in partial fulfillment of the requirements for the degree of Master of Science Degree in Climate Change and Energy | en_US |
| dc.description.abstract | COVID-19 has been an unprecedented situation that disrupted the stability of many socioeconomic dynamics all over the world and in particular on the energy sector in West-Africa. This study seeks to provide technical based analysis to inform policy decision-making and experts in the sector on how the pandemic impacted the countries in the region and actions to reduce vulnerability. This study analyses the impact of COVID-19 on Electricity Supply- Demand in Benin, Togo, Côte d’Ivoire, Senegal and Niger using both quantitative and qualitative data. The latter is based on semi-structured interviews targeting power utilities in these countries. A comparative assessment is conducted between the observed consumption and forecasted in 2020. Advanced forecasting methods with machine learning algorithms are explored including ARIMA, Prophet, ETS, TBATS, NNAR, GLMNET, Random Forest and hybrid ones which are regressed with climatic factors (temperature, humidity and solar radiation) and calendar effect (working days). The best models after the performance evaluation are the NNAR and GLMNET which show good measure compared to others. The assessment shows globally that despite the pandemic the demand has risen above forecast averaging 3.28%. Three distinct periods can be discerned from the time series: a pre-COVID where the demand rose in all countries, and slowed down as the pandemic intensified (in COVID period) and the post COVID period where the consumption rose up back as a result of the release of restriction measures (economic recovery). From one country to another, the recovery time can be longer or shorter. | en_US |
| dc.description.sponsorship | The Federal Ministry of Research, Technology and Space (BMFTR) | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | WASCAL | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Time series forecasting | en_US |
| dc.subject | West-Africa | en_US |
| dc.subject | Electricity | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Impact of COVID-19 on Electricity Supply-Demand in West-Africa | en_US |
| dc.type | Article | en_US |