Rademics Research Institute

Abstract

The increasing integration of IoT-enabled industrial power networks and renewable energy sources has created a critical demand for intelligent load forecasting and demand response optimization. Conventional forecasting models struggle to adapt to the dynamic nature of industrial energy consumption and the inherent variability of renewable generation. To address these challenges, this chapter explores the application of Neuro-Evolutionary Algorithms (NEAs) for real-time, adaptive load management. By combining the predictive capabilities of deep learning models with the optimization strength of evolutionary algorithms, NEA-driven frameworks enhance forecasting accuracy, optimize demand-side management, and ensure grid stability. The implementation of edge computing in industrial power systems further enables real-time data processing, reducing latency and enhancing responsiveness in energy distribution. The integration of cyber-physical systems (CPS) with AI-based forecasting mechanisms strengthens decision-making by dynamically adjusting to fluctuating industrial load patterns. Case studies demonstrate the effectiveness of NEA-driven load forecasting in improving energy efficiency, reducing operational costs, and optimizing the integration of renewable energy sources in industrial grids. The research highlights key challenges, including computational complexity, cybersecurity risks, and scalability constraints, while proposing future directions for the advancement of intelligent demand response strategies. By leveraging AI-driven optimization, industrial power networks can achieve enhanced sustainability, resilience, and cost-effective energy management.

Introduction

The increasing complexity of industrial power networks, driven by the integration of distributed energy resources and fluctuating demand patterns, necessitates advanced forecasting techniques for efficient load management [1-3]. Traditional forecasting models, including statistical and rule-based approaches, often fail to capture the nonlinear and dynamic nature of industrial energy consumption [4]. The rise of IoT-enabled infrastructures has facilitated real-time data acquisition, providing an opportunity to develop intelligent forecasting frameworks that adapt to varying industrial loads. Challenges such as unpredictable demand fluctuations, renewable energy intermittency, and grid stability concerns require innovative solutions that integrate artificial intelligence and optimization techniques [5]. By leveraging data-driven methodologies, industrial power systems can transition toward more adaptive and efficient energy management strategies, ensuring reliability and cost-effectiveness in modern power networks [6,7].

NEAs have emerged as a promising approach for enhancing load forecasting accuracy by integrating the predictive power of ANNs with the optimization capabilities of evolutionary algorithms (EAs) [8]. Traditional machine learning-based forecasting models often require manual tuning of hyperparameters and exhibit performance limitations when applied to large-scale industrial datasets. NEAs address these limitations by dynamically evolving model architectures, feature selection processes, and optimization strategies in response to real-time energy consumption trends [9]. By employing techniques such as genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE), NEA-driven forecasting models can improve adaptability and predictive precision in industrial settings [10-13]. This hybrid methodology enhances the ability to optimize demand response strategies, reduce peak loads, and integrate renewable energy sources more effectively within industrial power systems [14].