Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger
This study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), so...
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Online Access: | http://dx.doi.org/10.1155/2020/8460263 |
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doaj-ca18cfc54d884b4194822fe1bafe74222020-11-25T02:10:33ZengHindawi LimitedJournal of Energy2356-735X2314-615X2020-01-01202010.1155/2020/84602638460263Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in NigerAbdou Latif Bonkaney0West African Science Service Center on Climate Change and Adapted Land Use, Faculte des Sciences et Techniques, Universite de Niamey, NigerThis study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), solar radiation (SR), precipitation, and clearness index (K) are used. In addition, two heat indices, heat index (HI) and discomfort index (DI) are calculated to take into account the impacts of high humidity in conjunction with high ambient temperature. Hence, three different models were derived from the aforementioned variables. The three models have been tested using the k-folds cross-validation. Results show that the model with primitive variables such GDP per capita, Tmean, RH, SR, and WSP perform better than the other two models with a coefficient determination R2 equal to 0.87, 0.854, 0.833, and 0.551 for Niamey, Maradi, Zinder, and Agadez, respectively. According to the month considered, the mean absolute percentage error can give a small error for specific combinations of climate variables. The variables such as precipitation and clearness index are found to be not statistically significant.http://dx.doi.org/10.1155/2020/8460263 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Abdou Latif Bonkaney |
spellingShingle |
Abdou Latif Bonkaney Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger Journal of Energy |
author_facet |
Abdou Latif Bonkaney |
author_sort |
Abdou Latif Bonkaney |
title |
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger |
title_short |
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger |
title_full |
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger |
title_fullStr |
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger |
title_full_unstemmed |
Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger |
title_sort |
influence of climate and nonclimate parameters on monthly electricity consumption in niger |
publisher |
Hindawi Limited |
series |
Journal of Energy |
issn |
2356-735X 2314-615X |
publishDate |
2020-01-01 |
description |
This study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), solar radiation (SR), precipitation, and clearness index (K) are used. In addition, two heat indices, heat index (HI) and discomfort index (DI) are calculated to take into account the impacts of high humidity in conjunction with high ambient temperature. Hence, three different models were derived from the aforementioned variables. The three models have been tested using the k-folds cross-validation. Results show that the model with primitive variables such GDP per capita, Tmean, RH, SR, and WSP perform better than the other two models with a coefficient determination R2 equal to 0.87, 0.854, 0.833, and 0.551 for Niamey, Maradi, Zinder, and Agadez, respectively. According to the month considered, the mean absolute percentage error can give a small error for specific combinations of climate variables. The variables such as precipitation and clearness index are found to be not statistically significant. |
url |
http://dx.doi.org/10.1155/2020/8460263 |
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