Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Abstract

Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.