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Modelling and Forecasting Electricity Demand for Commercial and Industrial Consumers in Kenya to 2035
Commercial and industrial consumers are the largest users of electrical energy in Kenya. They play a central role in driving electricity demand by contributing to over 70% of the electricity demand in the country. Despite their consumption of electricity being the highest, there is a gap on the drivers of their demand. There are significant deviations between past official forecasts and actual putting into question the official forecast assumptions.This study adressed this gap by estimating the drivers of commercial and industrial electricity demand.The drivers included supply side constraints represented by hydro inflows hence contributing to literature. A demand forecast upto to the year 2035 was also undertaken and compared with the official forecast. Autoregressive distributed lag (ARDL) method and time series data from 1985 to 2016 was used in undertaking the analysis. The results indicated that commercial and industrial consumers’ electricity demand is income elastic. Other drivers include efficiency, electricity price and hydro inflows. A projection of the demand indicated the official forecast could be overstated and may need to be reviewed.
Modelling and Forecasting Electricity Demand for Commercial and Industrial Consumers in Kenya to 2035
Commercial and industrial consumers are the largest users of electrical energy in Kenya. They play a central role in driving electricity demand by contributing to over 70% of the electricity demand in the country. Despite their consumption of electricity being the highest, there is a gap on the drivers of their demand. There are significant deviations between past official forecasts and actual putting into question the official forecast assumptions.This study adressed this gap by estimating the drivers of commercial and industrial electricity demand.The drivers included supply side constraints represented by hydro inflows hence contributing to literature. A demand forecast upto to the year 2035 was also undertaken and compared with the official forecast. Autoregressive distributed lag (ARDL) method and time series data from 1985 to 2016 was used in undertaking the analysis. The results indicated that commercial and industrial consumers’ electricity demand is income elastic. Other drivers include efficiency, electricity price and hydro inflows. A projection of the demand indicated the official forecast could be overstated and may need to be reviewed.
Modelling and Forecasting Electricity Demand for Commercial and Industrial Consumers in Kenya to 2035
Njeru, Grace (author) / Gathiaka, John (author) / Kimuyu, Peter (author)
2020-05-31
doi:10.19044/esj.2020.v16n13p162
European Scientific Journal, ESJ; Vol 16 No 13 (2020): ESJ Social Sciences; 162 ; Revista Científica Europea; Vol. 16 Núm. 13 (2020): ESJ Social Sciences; 162 ; 1857-7431 ; 1857-7881 ; 10.19044/esj.2020.v16n13
Article (Journal)
Electronic Resource
English
DDC:
690
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