A comprehensive solution for electrical energy demand prediction based on auto-regressive models
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Abstract
Energy consumption and demand are two widely used terms necessary to understand the functioning of the different mechanisms used in electrical energy transactions. In this article, the design and construction of a comprehensive solution to forecast future trends in electricity transactions using the historical data and two auto-regressive models were considered. Simple linear regression and a complete model such as ARIMA. We compared these models to find which one best suits the type of data considering their strengths and weaknesses for this specific case. Finally, to complete the comprehensive solution, the results are presented to the final user. This solution is mainly aimed at professionals who carry out activities related to contracting and managing electricity supply in public institutions. This solution pretends to collaborate to reduce energy demand and therefore, consumption.
Resumen
Keywords
Prediction, ARIMA, Energy, Energy demand, Data capture, Auto-regressive models