Quantitative modelling of electricity consumption using computational intelligence aided design

High electricity consumption is of concern to the world for a variety of reasons, including its coupled social-economic-environmental impacts on the well-being of individuals, the social life and the federal energy policies. By using computational intelligence aided design (CIAD), this paper proposes a quantitative model to examine the long-term relationship between the annual electricity consumption and its major macroeconomic impact factors such as the gross domestic product, the electricity prices, the economic structure, and its resulting factors such as carbon dioxide emission. It develops a firefly algorithm with variable populations (FAVP) to obtain the parameters of the electricity consumption model through optimising two proposed trend indices: moving mean of the average precision (mmAP) and moving mean of standard derivation (mmSTD). The model is validated with empirical electricity consumption data in China between 1980 and 2012 based on which the error of approximations between 1980 and 2009 is 15% and the error of predictions between 2010 and 2012 is [-8%, -5%]. The main contributions of this paper lie in developing: (1) a novel quantitative model that can accurately predict the coupled social, economic and environmental impacts on the annual electricity demands; (2) a conceptual CIAD framework; (3) an FAVP algorithm; and (4) two new trend indices of the mmAP and the mmSTD. The findings of this paper can assist the decision makers in resolving the conflict between the energy consumption growth and the carbon emission reduction, which is essential for economic prosperity in the long run.