TY - GEN
T1 - A Comprehensive Solution for Electrical Energy Demand Prediction Based on Auto-Regressive Models
AU - Sáenz-Peñafiel, Juan José
AU - Luzuriaga, Jorge E.
AU - Lemus-Zuñiga, Lenin Guillermo
AU - Solis-Cabrera, Vanessa
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - ARIMA
KW - Auto-regressive models
KW - Data capture
KW - Energy
KW - Energy demand
KW - Prediction
UR - https://www.scopus.com/pages/publications/85094118561
U2 - 10.1007/978-3-030-59194-6_36
DO - 10.1007/978-3-030-59194-6_36
M3 - Contribución a la conferencia
AN - SCOPUS:85094118561
SN - 9783030591939
T3 - Advances in Intelligent Systems and Computing
SP - 443
EP - 454
BT - Systems and Information Sciences - Proceedings of ICCIS 2020
A2 - Botto-Tobar, Miguel
A2 - Zamora, Willian
A2 - Larrea Plúa, Johnny
A2 - Bazurto Roldan, José
A2 - Santamaría Philco, Alex
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Systems and Information Sciences, ICCIS 2020
Y2 - 27 July 2020 through 29 July 2020
ER -