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Energy is a very important issue to achieve Sustainable Development. Energy availability and affordability are key aspects for ending poverty and giving access to base commodities. In addition our daily lifes rely on electric appliances and gadgets to perform inumerous tasks so electricity price is a crucial issue due to its economic, environmental and social impacts. In this work it was studied the correlation of electricity price with the electricity mix from the several sources, GDP, energy productivity, electricity consumption per capita, fossil fuel reserves, and diesel price using Spearman correlation. The Kruskal-Wallis test was then applied considering the significant correlations and the macro variables that presented statistically significant differences were used to model electricity price. The logarithmic and linear model based on energy productivity to predict electricity price were the best models. The models were applied to the data and the average deviation was 10.3% and 11.7% which is satisfactory. Millions of people in the World are affected by electricity price and it is important to have models to predict electricity price to help in the decision-making process and in management.
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Energy is a very important issue to achieve Sustainable Development. Energy availability and affordability are key aspects for ending poverty and giving access to base commodities. In addition our daily lifes rely on electric appliances and gadgets to perform inumerous tasks so electricity price is a crucial issue due to its economic, environmental and social impacts. In this work it was studied the correlation of electricity price with the electricity mix from the several sources, GDP, energy productivity, electricity consumption per capita, fossil fuel reserves, and diesel price using Spearman correlation. The Kruskal-Wallis test was then applied considering the significant correlations and the macro variables that presented statistically significant differences were used to model electricity price. The logarithmic and linear model based on energy productivity to predict electricity price were the best models. The models were applied to the data and the average deviation was 10.3% and 11.7% which is satisfactory. Millions of people in the World are affected by electricity price and it is important to have models to predict electricity price to help in the decision-making process and in management.
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