Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources

Abstract

The long-term goals achievement of sustainable development based on consumption of electrical energy is possible through the use of renewable energy sources. The objectives of this study include (a) long-term forecasting of Electrical Energy consumption (EEC) for sample countries with developed and developing economies, and (b) analyzing of different scenarios based on the use of solar and wind energy sources with 1%, 2% and 3% of the EEC. Artificial neural network (ANN) modeling with socio-economics data of the energy balance sheet last 30 years (1990–2019) as input data contain Gross Domestic Product (GDP), Population (POP), Import (IMP), Export (EXP), and EEC are used in order to forecast the EEC in the long-term (2020–2050). The United States and the OECD as developed economies and China, India, and Iran as developing economies are the countries under study. The structure of ANN is optimized for long-term EEC forecasting based on PSO and E-PSO algorithms. For both types of inputs and economies, the results demonstrate that E-PSO – ANN model can be used by SRE-3% scenario, which finally leads to a reduction of 55% and 54% in the EEC and amount of CO2 emissions in average according to the Paris Agreement (PA) goals, respectively.