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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Main Author: Abdou Latif Bonkaney
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Journal of Energy
Online Access:http://dx.doi.org/10.1155/2020/8460263
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spelling 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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