The study highlights how artificial intelligence, blockchain, and smart systems can enable buildings to actively support smart grids, overcoming regulatory and technical challenges for economic and environmental benefits.

The study provides an in-depth analysis of technological advancements for enabling buildings to actively participate in smart grid demand-response programs. It was conducted as part of the DEDALUS project by  the Technical University of Cluj-Napoca (TUC) and the National Technical University of Athens (NTUA), and recently published in Energy & Buildings.

The research offers valuable insights for academics, industry leaders and policymakers, shedding light on both opportunities and challenges in integrating advanced solutions into smart buildings.

The analysis underscores the critical role of innovative technologies such as AI-driven energy prediction models, blockchain for secure and transparent transactions, and interoperable systems that enable seamless communication between devices. These advancements are pivotal in enhancing energy flexibility and ensuring the efficient integration of buildings into smart grids. Furthermore, the transformative potential of decentralized technologies, particularly blockchain, is highlighted as a key enabler in fostering transparent and secure energy marketplaces.

However, the adoption of demand response programs is hindered by several barriers, including ambiguous regulatory frameworks, technical compatibility issues among building systems, and persistent concerns regarding data privacy. It is essential to address these obstacles to fully unlock the potential of these initiatives.

Despite these challenges, the economic and environmental advantages of demand response programs are significant. By aligning energy consumption with renewable energy production, such programs not only reduce costs for consumers but also enhance grid stability and contribute to ambitious decarbonisation targets.

These innovations enable buildings to evolve from passive energy consumers into active contributors to grid stability and sustainability.

“At NTUA, we are committed to advancing research that drives the energy transition through cutting-edge technology. Our collaboration with TUC has enabled us to explore innovative solutions for optimising demand response in smart grid-integrated buildings. By reviewing technology enablers and addressing innovation challenges, we aim to contribute to a more sustainable and efficient energy future,” stated Elissaios Sarmas, researcher ad DSS Lab of NTUA.

“This research emphasizes how emerging technologies like AI and blockchain can fundamentally transform buildings into proactive players within energy grids and demand-response programs. By unlocking their potential, we can accelerate Europe’s journey toward a more sustainable and resilient energy ecosystem. However, achieving this requires a concerted effort among stakeholders—policymakers, industry, and researchers—to address regulatory gaps, foster collaboration, and implement unified strategies for seamless integration,” explained Tudor Cioara – professor and senior researcher at the Distributed Systems Research Laboratory of Technical University of Cluj-Napoca. 

For full access to the study, click here for download

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This NTUA's paper presents a machine learning framework to optimize demand-response programmes by clustering electricity consumers. Conducted with DEDALUS, it offers a scalable solution for targeted energy management.

The National Technical University of Athens (NTUA), one of the DEDALUS scientific partners, has completed a study on grouping residential electricity consumers, based on their historical electricity consumption, to create more targeted demand-response programmes.

This grouping will be used in almost every DEDALUS service at the end of the day, making the services more targeted per group.

Specifically, this paper introduces a machine learning-based framework to optimize demand response programmes. Using data from nearly 5,000 households in London, four clustering algorithms—K-means, K-medoids, Hierarchical Agglomerative Clustering, and DBSCAN—were evaluated to identify groups with similar consumption patterns.
The problem was reframed as a probabilistic classification task, leveraging Explainable AI to improve model interpretability. The optimal number of clusters was found to be seven, although two clusters, comprising around 10% of the data, exhibited high internal dissimilarity and were excluded from further consideration. This framework offers a scalable solution for utility companies to enhance the targeting and effectiveness of demand response initiatives.

“Our research aims to tackle a key challenge in energy management: efficiently identifying and classifying household energy consumption patterns to enhance the implementation of Demand Response programs. Optimizing household energy use is increasingly critical, both for promoting environmental sustainability and for enabling utility companies to deliver more targeted and effective DR solutions. This work aligns with the overarching objectives of the DEDALUS project, which seeks to expand residential participation in DR programs across Europe by bringing together key stakeholders and advancing smarter energy management strategies”, said Vasilis Michalakopoulos – one of the paper’s authors. 

The paper, in Open Access, is available here for download.

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