article · Computing
An extensive review of more than two hundred studies published between 2014 and 2024 evaluates the role of machine learning and deep learning within smart energy management systems. These computational approaches deliver high precision and strong predictive performance in load forecasting, while also enabling effective demand response mechanisms across electrical networks. Integrating these models into energy management frameworks requires stronger data infrastructure, thorough training and validation processes, and coordinated collaboration between researchers, industry partners, and policymakers. However, current deployments remain limited in real-world settings. Key technical hurdles include poor data availability, inconsistent data quality, and the persistent challenge of model interpretability. Overcoming these barriers will be essential to expanding the practical adoption of artificial intelligence in energy systems and establishing more reliable, sustainable network operations.
Modern electrical grids face mounting pressure to balance fluctuating supply and demand sustainably. Harnessing artificial intelligence helps energy managers forecast demand with greater accuracy and adjust network responses efficiently. Resolving existing bottlenecks around data reliability and practical deployment will allow energy sectors to operate more cleanly, avoid waste, and build resilient infrastructure capable of meeting future power needs.
The findings are relevant to utility providers, energy management software developers, and grid operators seeking to automate load forecasting and demand response. Because the literature shows limited real-world implementations alongside challenges in data quality and model interpretability, the technology appears to be largely in early-stage research rather than near-market deployment. Commercial application will require targeted investments in data infrastructure and closer industry collaboration to move models into operational environments.
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This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.
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DOI: 10.1007/s00607-025-01485-0
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