TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Modelling for forecasting energy consumption using SBO optimization and machine learning

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pp. 605–612Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1598XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1598
Pages:
605–612

Abstract

Forecasting the future electrical load of a single apartment, a grid, an area, or even an entire country is known as load forecasting, which aims to predict future load demand. Using residential data for model training and a School-Based Optimization approach for optimising the process and computing energy consumption and occupant comfort, the proposed approach has 3 components: (1) machine learning model for low energy consumption; (2) occupant behaviour models; and (3) occupant comfort models. The experimental findings indicated that behavioural energy savings were possible, with occupant comfort significantly increased. Machine learning (ML) methods have recently contributed very well in the advancement of the prediction models used for energy consumption. AdaBoost models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional forecasting which is utilized in models.

Keywords

Subject Classifications

93-XX94-XX

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