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Nakicenovic, N., Grübler, A., Gaffin, S., Jung, T.T., Kram, T., Morita, T., Pither, H., Riahi, K., Schlesinger, M., Shukla, P.R., Van Vuuren, D., Davis, G., Michaelis, L., Swart, R. and Victor, N. (2003) IPCC SRES Revisited: A Response. Energy and Environment, 14, 187-214.
https://doi.org/10.1260/095830503765184592

has been cited by the following article:

• TITLE: Modelling the Impact and Effects of Climatic Variability on Electricity Energy Consumption in the Yendi Municipality of Ghana

  AUTHORS: Wahab A. Iddrisu, Sampson T. Appiah, Khalid Abdul-Mumin, Abdul-Rahman Abdul-Samed

  KEYWORDS: Electricity Energy Consumption, Vector Autoregression, Climatic Variables

  JOURNAL NAME: Open Journal of Energy Efficiency, Vol.9 No.1, March 4, 2020

  ABSTRACT: One of the cherished assets of every economy is electricity since it has proven to be the major source of energy for industrialization. Developing economies like Ghana have suffered the downside of poor management of the already inadequate electrical energy at its disposal. This is as a result of limited research into factors that influences electricity energy consumption, most importantly, the effects of climatic variables on electricity energy consumption. This research work explores the causal connection between climatic variables and electricity energy consumption, and develops a Vector Auto Regression (VAR) model to determine the influence of the climatic variables in forecasting electricity energy consumption in Yendi Municipality in the northern region of Ghana. The climatic factors considered in this work are; Rainfall (Rain), maximum temperature (Tmax), Sunshine (Sun), Wind (wind) and Relative Humidity (RH). The Granger causality tests employed in this work revealed that aside from Relative Humidity, the end energy consumption is affected by the other four climatic factors under consideration. The impulse response was used to ascertain the active interaction among electricity energy consumption and the climatic variables. The impulse response of electricity energy consumption to the climatic variables indicates a maximum positive effect of Temperature and Sunshine on electricity energy consumption in March and September respectively. The VAR model was also used in forecasting future consumption of electricity energy. The results indicate excellent forecasts of electricity energy consumption for the first four months of 2019.