Work

Enhanced machine learning algorithm for predicting energy consumption in smart buildings

Year: 2026

Type: article

Abstract: Energy consumption in buildings represents a substantial share of global energy use and underscores the need for intelligent solutions to improve efficiency. Accurate prediction of heating and cooling loads is essential for optimizing energy usage in smart buildings and supporting broader sustainability goals. SRSm is a new variant of the Special Relativity Search (SRS) algorithm that incorporates a mutation phase to increase population diversity, enhance exploration, and reduce the risk of local optima. This variant is used to optimize a neural network for the accurate prediction of heating and cooling loads. The proposed model is evaluated using several statistical performance metrics and compared with conventional and advanced optimization techniques. The results show that SRSm consistently achieves competitive predictive accuracy. The integration of the mutation mechanism improves predictive performance and leads to high accuracy in both heating and cooling load estimation during the testing phase.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2026.100903

Authors: Ahmed A. Ewees, Shaymaa E. Souror, Abdullah M. Alharthi, Mohammed M. Alshahrani

Institutions: University of Bisha, King Faisal University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,200