ANN‐Enhanced Energy Reference Models for Industrial Buildings: Multinational Company Case Study

Abstract

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector.