Publications

Date: 2026

Publication Type: Conference paper

Author(s): João Mello, Fábio Retorta, José Villar, João T. Saraiva

Abstract: This work proposes a unified methodology to analyse, classify, and compare Local Electricity Market (LEM) designs. This methodology integrates insights from the LEM design related literature with established principles from market-design theory. Existing proposals differ widely in their objectives, assumptions, market mechanisms, and integration with distribution system operation. To address this heterogeneity, we introduce a structured design-analysis framework inspired by mainstream market design literature, emphasizing market diagnosis, mechanism proposal, pilot testing, and implementation. A systematic review of 23 papers identifies nine core contributions relevant to LEM design. From these contributions, we extract explicit and implicit design choices and reorganize into a clear taxonomy that covers market participants, products and services, clearing structures, market timing, allocation and pricing mechanisms, and assessment criteria. Our analysis reveals recurring conceptual gaps in the literature, particularly regarding institutional rules, regulatory constraints, delivery commitments, and settlement assumptions. These elements are essential for real-world feasibility. The proposed methodology offers a consistent reference for future LEM research, enabling clearer comparisons across models and supporting the development of regulation-aware and practically implementable market designs.

Link: https://doi.org/10.1109/EEM68581.2026.11589707 

Date: 2026

Publication Type: Conference paper

Author(s): João Mello, António Sérgio Faria, Luís Rodrigues, Tiago Soares, José Villar

Abstract: This paper proposes a unified market design integrating a Post-Delivery Local Energy Market (PD-LEM) and a Local Flexibility Market (LFM) in Energy Communities (EC) operating under the Portuguese Collective Self-Consumption (CSC) framework. The design builds on the ENPOWER project and addresses coordination challenges between prosumers, suppliers, and grid operators in simultaneous energy sharing and flexibility provision. The main contribution is the definition of essential LFM design elements, including agents, roles, contractual relationships, and product specifications, many of which are required in the absence of a national reference LFM model. A flexibility delivery timeline is defined, covering the full process from Distribution System Operator (DSO) request to activation and settlement. The paper also proposes a mechanism to integrate LFM-committed resources into PD-LEM clearing through baseline-defined bids. Finally, an integrated settlement framework is introduced, combining PD-LEM transactions, CSC allocation, flexibility remuneration, and grid-related payments.

Link: https://doi.org/10.1109/EEM68581.2026.11589751 

Date: 2026

Publication Type: Conference paper

Author(s): CAM Silva, JP Paulos, V Michalakopoulos, AS Faria, R Friães, J Villar, T Soares, E Sarmas

Abstract: The integration of renewable energy and dynamic pricing in European electricity systems requires innovative approaches to manage residential flexibility. This paper presents an elasticity-aware clustering and optimisation framework to jointly model behavioural and asset-based flexibility, addressing gaps in current demand-side management strategies. The methodology comprises three tools: (i) causal inference to estimate consumers’ price elasticities, isolating the impact of price variations on consumption; (ii) unsupervised clustering using both elasticity estimates and electric water heater (EWH) load profiles, capturing heterogeneity in flexibility; and (iii) cluster-level optimization for EWH scheduling, minimizing energy usage while preserving user comfort. Simulation results demonstrate measurable reductions in electricity usage across clusters (around 75%). The framework supports aggregators and retailers in designing differentiated market strategies, enhancing coordination in future flexibility markets. By bridging behavioural and operational flexibility, this approach offers relevant insights for residential demand-side management.

Link: https://doi.org/10.1109/EEM68581.2026.11589607 

Date: 2025

Publication Type: Conference paper

Author(s): L Rodrigues, R Silva, P Macedo, S Faria, F Cruz, J Paulos, J Mello, T Soares, J Villar

Abstract: Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a costeffective investment. Meanwhile, the growing share of nondispatchable renewable generation demands more flexible energy systems. Local flexibility markets (LFMs) are emerging as effective mechanisms to procure this flexibility, granting ECs a new revenue stream. Since sizing with flexibility becomes a highly complex problem, we propose a 2 -stage methodology for estimating DERs size in an EC with collective self-consumption, flexibility provision and cross-sector (CS) assets such as thermal loads and electric vehicles (EVs). The first stage computes the optimal DER capacities to be installed for each member without flexibility provision. The second stage departs from the first stage capacities to assess how to modify the initial capacities to profit from providing flexibility. The impact of data clustering and flexibility provision are assessed through a case study.

Link: https://doi.org/10.1109/EEM64765.2025.11050164 

Date: 2025

Publication Type: Conference paper

Author(s): Luís Rodrigues, João Mello, Ricardo Silva, Sérgio Faria, Fábio Cruz, José Paulos, Tiago Soares, José Villar

Abstract: Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered.

Link: https://doi.org/10.1109/EEM64765.2025.11050324 

Date: 2024

Publication Type:  Conference paper

Author(s): C. Pereira, J. Villar

Abstract: Ensuring robust semantic interoperability is essential for efficient data exchange in the energy sector. This paper introduces SEMAPTIC, a lightweight framework that simplifies semantic interoperability by providing a standardized approach for attaching metadata to exchanged data. SEMAPTIC utilizes ontologies to define the meaning of data elements and employs a new structured metadata map to guide data interpretation. This approach simplifies data exchange, minimizes maintenance effort, and fosters unambiguous data understanding across heterogeneous systems. Compared to traditional methods that often require complex data transformations, SEMAPTIC offers greater flexibility and reduced overhead. The paper explores the benefits of SEMAPTIC, including simplified integration, minimal maintenance, enhanced interoperability, reduced misinterpretation, facilitated data reuse, and future-proofing. A practical example showcases how SEMAPTIC enriches a JSON data structure with semantic context without the need of modifying the original structure and without inflating data size. Finally, the importance of well-defined ontologies is emphasized, highlighting how SEMAPTIC empowers the energy sector to achieve seamless and reliable data exchange, paving the way for a more efficient and intelligent energy ecosystem.

Link: https://doi.org/10.1049/icp.2024.4751 

Date: 2025

Publication Type: Conference paper

Author(s): J. C. Agrela, T. Soares, J. Villar, I. Rezende

Abstract: The increasing integration of renewable energy sources and decentralized generation requires demand-side flexibility to improve grid stability and balance local energy flows. Local Flexibility Markets (LFMs) provide a framework for optimizing flexibility transactions within energy communities. This paper presents a model for quantifying and pricing residential resources flexibility, enabling prosumers to submit bids in an LFM managed by the Community Manager. The methodology relies on a linear optimization problem, where a Home Energy Management System first determines optimal consumption baselines. Then an iterative sensitivity analysis estimates upward, and downward flexibility bands and sets offer prices per resource. The market operates as two asymmetric voluntary pools, clearing flexibility offers and requests. Results show that Battery Energy Storage Systems and Electric Vehicles provide the most effective flexibility, significantly reducing energy costs. Future research should improve pricing mechanisms and scalability to support LFM adoption in different residential settings.

Link: https://doi.org/10.1109/eem64765.2025.11050150 

Date: 2025

Publication Type:  Conference paper

Author(s): Diogo Antunes; Tiago Soares; Hugo Morais

Abstract: As energy systems evolve, protecting and empowering consumers is vital, enabling participation in decentralized electricity markets and maximizing benefits from energy resources. The integration of Distributed Energy Resources (DER) and Renewable Energy Sources (RES) fosters new energy communities, shifting from centralized systems to distributed structures. Consumers can sell excess production to neighbors, increasing income, reducing bills, and advancing energy transition goals. This paper proposes a community-based peer-topeer (P2P) energy market model that reduces costs while respecting network constraints. Using the Alternating Direction Method of Multipliers (ADMM), ensures privacy enhancement, decentralization, and scalability. The Relaxed Branch Flow Model (RBFM) manages constraints, and Electric Vehicles (EVs) reduce imports and costs through strategic discharging. Tested on a 33-bus distribution network, the ADMM-based approach aligns closely with a centralized benchmark, showing minor discrepancies while maintaining system reliability. This model underscores the potential of decentralized markets for consumercentric, flexible, and efficient energy trading.

Link: https://doi.org/10.1109/EEM64765.2025.11050345 

Date: 2025

Publication Type: Conference paper

Author(s): N. Matsagkos, E. Kanellou, V. Michalakopoulos, E. Sarantinopoulos, V. Marinakis

Abstract: A series of unprecedented energy and geopolitical shocks led the European Union to accelerate its commitment to a just green energy transition. Efforts for diversification of energy supply, shifting from centralised to decentralised energy systems, and increasing the Renewable Energy Sources while reducing the reliance on fossil fuels, led to the upgrowth of energy communities. Within this evolving landscape, energy communities have emerged not only as tools for enhancing energy resilience and sustainability but also for citizen empowerment and energy democracy. The aim of this work is to introduce a list of 25 indicators in order to evaluate and assess the impact of implemented services and strategies by different energy communities, and thus the impact of these communities, from a social, economic/technical and environmental perspective. The Key Performance Indicator framework is applied to four diverse energy communities from different European Union member states, each with distinct characteristics and missions. This research offers insights into the understanding of energy communities' evaluation but also underlines the importance of refining Key Performance Indicators to ensure holistic evaluation.

Link: https://doi.org/10.1109/iisa66859.2025.11311339 

Date: 2026

Publication Type: Journal article

Author(s): Julia Blanke, Adam Hearn, Iljana Schubert, Alexandra Revez, Annika Sohre

Abstract: The development of digital tools for energy communities is crucial as these can play a significant role increasing efficiency and leading to less grid congestion. However, these tools frequently suffer from a disconnect between technical design and end-user social contexts, leading to poor adoption and engagement. This study bridges this gap by providing a new empirically robust methodology for mapping and evaluating how social dimensions, derived from a scoping literature review and participatory engagement, can be applied to energy tool development. Through six deliberative workshops and two focus groups across three European pilot sites, we identified 19 key social dimensions, including trust, transparency, education, and social values, that influence community member motivation and behaviour. This forms the basis of a social dimensions framework which can be used to evaluate energy tools and improve their development and uptake. We then exemplify the application of the social dimensions framework to assess three distinct digital tools from the Enpower project: a nudging app, a planning tool, and a citizen renewable platform. The analysis maps specific application features against the social dimensions, revealing strengths and critical gaps in how each tool supports user motivations and collective goals. Our findings show that moving beyond standard usability evaluations to include a behaviourally informed, socially grounded assessment provides actionable insights for developers. This approach facilitates the translation of qualitative social research into concrete technical specifications, ultimately promoting the creation of more effective, engaging, and socially aligned digital tools that can better support the energy transition within communities.

Link: https://doi.org/10.12688/openreseurope.23379.1 

Date: 2026

Publication Type:  Journal article

Author(s): D. Avgoloupis, V. Michalakopoulos, E. Sarmas, V. Marinakis, M. Couto, A. Coccia, M. Jimenez

Abstract: Household energy demand data are essential for designing load-shifting strategies, storage solutions and demand response programs. This is the first publicly available dataset that integrates multiple household-level energy and mobility metrics. These include grid imports and exports (30-minute intervals), rooftop photovoltaic (PV) production (30-minute intervals), EV charging sessions, and detailed journey logs (start time, end time, distance, and duration) from the same households of a pilot energy community located on the Dingle Peninsula, Ireland. The dataset includes four volunteer households, each equipped with a 2.1 kWp rooftop PV system, 5 kWh Sonnen battery, a Hyundai Kona Electric (64 kWh), a Pulsar Plus EV charger (7.4 kW AC), and a Mitsubishi Electric Ecodan air-source heat pump.While grid and PV data span three years, EV mobility logs cover February 2021 to January 2022. Battery telemetry and heat pump demand data are available for the final six to eight months of the observation period (mid-2021 to early 2022), enabling detailed whole-home analysis for that specific window.

Link: https://doi.org/10.1038/s41597-026-07186-3  

Date: 2026

Publication Type: Journal article

Author(s): Ane Feijoo-Arostegui, Luís Rodrigues, Haizea Gaztañaga, José Villar, Tiago Soares, Ander Goikoetxea

Abstract: The increasing deployment of individual and collective self-consumption systems is reshaping Energy Management Systems (EMSs) under evolving regulatory frameworks. This paper presents a techno-economic comparison between a centralized EMS and a decentralized EMS for flexible resources dispatching and sharing under collective self-consumption schemes. The centralized EMS is formulated as a Mixed-Integer Non-Linear Programming (MINLP) optimization problem, whereas the decentralized EMS employs a rule-based algorithm that requires no information exchange among members. Both strategies have been evaluated under the Spanish regulatory framework, a) using fixed allocation coefficients and b) introducing improvements borrowed from the Portuguese regulation, selected as a benchmark due to its advanced regulatory maturity. For the case of ex-ante allocation coefficients computation, an optimization-based methodology is proposed combining Mixed-Integer Linear Programming (MILP) with data clustering techniques. Results indicate that both EMS architectures achieve comparable energetic performance. The centralized EMS achieves the highest levels of self-consumption, self-sufficiency and energy sharing, particularly when proportional allocation coefficients are used, while the decentralized EMS performs closely. From an economic perspective, the centralized EMS provides the highest cost reductions, while the decentralized EMS yields lower economic savings but with significantly less computational effort, with runtimes up to eighteen times shorter. These findings highlight a clear trade-off between economic optimality and computational efficiency, positioning decentralized EMS solutions as a scalable and privacy-preserving alternative for individual self-consumers transitioning to collective self-consumption schemes in evolving regulatory frameworks.

Link: https://doi.org/10.1016/j.apenergy.2026.128014 

Date: 2025

Publication Type: Journal article

Author(s): A. Xenakis, F. D. Mexis, E. Kanellou, H. Doukas

Abstract: Energy poverty remains a pressing social and policy challenge across Europe, particularly in countries like Greece, where economic disparities and climatic conditions vary significantly between regions. This paper presents a scalable and transparent methodological framework for assessing energy poverty using the 10% indicator based on the actual energy expenditure, calculated exclusively from open-access statistical data. The study estimates annual 10% metric values from 2012 to 2023 at both national and regional levels, covering all 13 administrative regions of Greece. By integrating key variables such as household energy consumption, energy prices, income, population distribution, and heating degree days, the framework enables regional comparisons without relying on costly or inaccessible household-level data. The results reveal substantial regional disparities, with northern and mountainous areas consistently exhibiting higher 10% index values due to lower incomes and greater heating needs, while southern and island regions remain less affected. Despite national values remaining below the conventional 10% threshold throughout the period, the findings highlight that significant areas of vulnerability persist. The proposed approach offers a practical tool for policymakers to identify high-risk areas, prioritize building renovation efforts, and support socially inclusive energy transition strategies across the EU.

Link: https://doi.org/10.3390/en18215787 

Date: 2026

Publication Type: Journal article

Author(s): Afonso Ferreira, António Sérgio Faria, Tiago Soares

Abstract: Heating, Ventilation, and Air Conditioning (HVAC) systems represent a major share of building energy consumption, creating both operating cost and emissions reduction challenges for energy communities. This work proposes an optimisation-based management framework for a community of buildings, integrating HVAC operation with renewable generation, battery storage, and demand-side flexibility. The methodology employs a mixed-integer linear programming model to coordinate thermal and electrical energy flows, considering indoor comfort constraints, equipment dynamics, and market price signals. The framework is validated through a case study using real demand, weather, and market data, comparing baseline and optimised operation under varying seasonal conditions. Results demonstrate significant reductions in total operating cost and peak demand of the energy community, alongside improved usage of renewable generation and reduced reliance on the grid, without compromising thermal comfort. The proposed approach highlights the potential of coordinated HVAC scheduling in energy communities as a pathway toward more cost-efficient and sustainable building operation.

Link: https://doi.org/10.1007/s40866-026-00345-w 

Date: 2024

Publication Type: Journal article

Author(s): Giulia Taromboli, Tiago Soares, José Villar, Matteo Zatti, Filippo Bovera

Abstract: Recently, the uptake of renewable energy has surged in distribution networks, particularly due to the cost-effectiveness and modular nature of photovoltaic systems. This has paved the way to a new era of user engagement, embodied by individual and collective self-consumption, and promoted by the EU Directive 2018/2001, which advocates for the establishment of Renewable Energy Communities. However, the transposition of this directive varies across Member States, resulting in specific rules for each country. In this work, the impact that different energy sharing models have on the same community is quantitatively assessed. The policy analysis focuses on the regulation of two countries, Italy and Portugal, chosen for the specular ways in which their models operate, respectively virtually and physically. The analysis is supported by a suite of tools which includes two optimization problems for community's operations, one for each analysed regulation, and a set of consumer protection mechanisms, to ensure no member is losing money while in community. Results demonstrate that the sharing model impacts community's optimal operations, optimal battery size and configuration, and members' benefit. As these models are sensitive to different variables, personalized interventions at national level are required.

Link: https://doi.org/10.1016/j.enpol.2024.114399 

Date: 2026

Publication Type: Journal article

Author(s): David Reis, Luís Rodrigues, José Villar, Tiago Soares

Abstract: Renewable Energy Communities (RECs) are a promising driver for energy system decarbonization, engaging end consumers in energy markets, promoting local renewable generation and affordability, and enhancing community resilience. This paper presents a methodology for planning REC by optimally sizing distributed energy resources (DERs), including conventional resources such as photovoltaic (PV) systems and battery energy storage systems (BESSs), but also power-to-hydrogen (P2H) systems composed of an electrolyzer (EC), hydrogen tank, and fuel cell (FC). A mixed-integer linear programming (MILP) model is used to minimize DER investments and REC operational costs, using a reduced input data set based on clustering techniques to decrease complexity and improve computational efficiency. Then, a detailed MILP operation model is used to assess the REC performance with the newly sized DERs under different scenarios using conventional indicators such as the net present value (NPV) and the payback period. A sensitivity analysis is also conducted on electricity costs, EC costs and the hydrogen selling price for an external combined heat and power (CHP) client. Results show that integrating P2H systems increases NPV and provides new revenue opportunities, thereby reinforcing their role as a complementary technology to PV and BESS within RECs.

Link: https://doi.org/10.1016/j.epsr.2026.113343 

Date: 2025

Publication type: Conference Paper

Author(s): Efstathios Sarantinopoulos, Vasilis Michalakopoulos, Nektarios Matsagkos, Eleni Kanellou, Elissaios Sarmas, Vangelis Marinakis

Abstract: Remote and non-interconnected island communities face persistent energy management challenges due to geographic isolation, constrained infrastructure, and dependence on imported fossil fuels. The integration of renewable energy sources (RES), particularly photovoltaics (PV), presents a promising pathway toward energy autonomy. However, the intermittent nature of solar energy complicates its efficient utilization. This study introduces a novel self-consumption optimization framework designed to facilitate intelligent load shifting at both the community and household levels, with a specific focus on the island of Chalki, Greece. The proposed system integrates PV generation and electricity consumption forecasting using Long Short-Term Memory (LSTM) models, coupled with non-intrusive load monitoring (NILM) techniques for disaggregating coolingrelated demand. To ensure user comfort, thermal modeling is employed using customized Predicted Mean Vote–Predicted Percentage Dissatisfied (PMV–PPD) metrics. At the community scale, a heuristic algorithm identifies optimal time windows for load shifting based on projected PV surplus. Concurrently, household-level cooling demand is managed through a multiobjective optimization approach that balances thermal comfort with the availability of renewable generation. The framework is implemented as a user-centric web application, providing both a centralized energy management dashboard for the community and individualized control tools for end-users. The system aims to enhance renewable energy self-consumption and promote sustainable, resilient energy practices in insular communities. Index Terms—Photovoltaic systems, self-consumption optimization, energy management, load shifting, renewable energy, thermal comfort, machine learning, island microgrids

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Date: 2025

Publication type: Conference Paper

Author(s): Nektarios Matsagkos, Efstathios Sarantinopoulos, Vasilis Michalakopoulos, Eleni Kanellou, Vangelis Marinakis

Abstract: A series of unprecedented energy and geopolitical shocks led the European Union to accelerate its commitment to a just green energy transition. Efforts for diversification of energy supply, shifting from centralised to decentralised energy systems, and increasing the Renewable Energy Sources while reducing the reliance on fossil fuels, led to the upgrowth of energy communities. Within this evolving landscape, energy communities have emerged not only as tools for enhancing energy resilience and sustainability but also for citizen empowerment and energy democracy. The aim of this work is to introduce a list of 25 indicators in order to evaluate and assess the impact of implemented services and strategies by different energy communities, and thus the impact of these communities, from a social, economic/technical and environmental perspective. The Key Performance Indicator framework is applied to four diverse energy communities from different European Union member states, each with distinct characteristics and missions. This research offers insights into the understanding of energy communities' evaluation but also underlines the importance of refining Key Performance Indicators to ensure holistic evaluation.

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Date: 2023

Publication type: Conference Paper

Author(s): Symeon Chorozoglou, Elissaios Sarmas, Vangelis Marinakis

Abstract: Energy digitization holds significant importance for various energy applications, encompassing aspects like production, consumption, and distribution within power grids. The digital transformation of energy plays a pivotal role in enhancing the integration of Artificial Intelligence (AI) into energy management systems, leveraging extensive datasets. The development of AI systems and the utilization of Machine Learning (ML) techniques empower users with precise predictions related to renewable energy production, thereby expediting the shift towards clean energy. Nevertheless, the effective use of data demands a high level of expertise, thereby excluding energy stakeholders from the benefits modern technologies offer. In this paper, we introduce an AI forecasting system designed to bridge the knowledge gap in data processing methods and ML models for energy stakeholders. This system focuses on delivering a user-friendly interface for photovoltaic (PV) production forecasting by automating the entire ML operations pipeline. Consequently, users can obtain data-driven model results without the need to manually code all the requisite steps for model training and fine-tuning. To demonstrate the system’s capabilities, we provide an experimental application using real PV data from a Portuguese aggregator.

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Date: 2024

Publication type: Conference Paper

Author(s): Nektarios Matsagkos, Eleni Kanellou, Afroditi Fragkiadaki, Vasilis Michalakopoulos, Vangelis Marinakis, Haris Doukas

Abstract: The need to ensure a just energy transition and shift towards decentralised energy systems is becoming crucial. The European Commission outlines the need for energy independence, based on two key pillars: (a) diversifying gas supplies; (b) reducing faster the use of fossil fuels in homes, buildings, industry, and power system, by increasing energy efficiency gains and renewables. Energy communities can be a pivotal mechanism in achieving this transition. However, the current social practices, technologies adoption and business models are not sufficiently mature to enable energy citizenship's practices scaling up and replication. There is a need to transform traditional passive energy consumers into active energy citizens enabling them to take full control of their energy usage. To do so engaging with all the principal actors of the energy value chain, and allowing them to achieve energy savings is needed, while increasing their energy efficiency and self-consumption optimisation and transforming energy consumers and communities to digitally enhanced and grid-friendly ones.

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Date: 2024

Publication type: Conference Paper

Author(s): João Mello, Luís Rodrigues, José Villar, João Saraiva

Abstract: Energy allocation rules are one of the core aspects of collective self-consumption (CSC) regulations. It allows final consumers to share their surplus generation with other CSC members, while keeping their full rights as consumers, i.e., maintaining a supply contract with the retailers of their choice. Some European Union member states regulations use allocation coefficients so that local allocations are integrated with wholesale settlement and directly affect the retailers’ billing. Several AC methods have been proposed so far, each one adapted to distribution system operators’ settlement procedures with specific rules that can impact the benefits that each CSC member obtain. This paper analyses, assesses and compares two relevant AC methods, namely pre-delivery fixed AC and post-delivery dynamic AC, by developing a settlement formulation for a community with members with flexible assets and different opportunity costs. AC policy recommendations based on findings are provided.

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Date: 2024

Publication type: Journal article

Author(s): Elissaios Sarmas, Afroditi Frangkiadaki, Vangelis Marinakis

Abstract: Smart meter data provide an in-depth perspective on household energy usage. This research leverages on such data to enhance demand response (DR) programs through a novel application of ensemble clustering. Despite its promising capabilities, our literature review identified a notable under-utilization of ensemble clustering in this domain. To address this shortcoming, we applied an advanced ensemble clustering method and compared its performance with traditional algorithms, namely, K-Means++, fuzzy K-Means, Hierarchical Agglomerative Clustering, Spectral Clustering, Gaussian Mixture Models (GMMs), BIRCH, and Self-Organizing Maps (SOMs), across a dataset of 5567 households for a range of cluster counts from three to nine. The performance of these algorithms was assessed using an extensive set of evaluation metrics, including the Silhouette Score, the Davies–Bouldin Score, the Calinski–Harabasz Score, and the Dunn Index. Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. Unlike approaches that seek the algorithmically optimal number of clusters, our method proposes a practical six-cluster solution designed to meet the operational needs of utility providers. For this case, the best performing algorithm according to the evaluation metrics was ensemble clustering. This study is further enhanced by integrating Explainable AI (xAI) techniques, which improve the interpretability and transparency of our clustering results.

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Date: 2025

Publication type: Conference paper

Author(s): Mário Couto, Mariana Jimenez, Peter Richardson, Alessio Coccia, Alexandra Revez, Julia Blanke, Deirdre de Bhailís, Jonathan Sandham, John Walsh

Abstract: Social engagement and data sharing have a critical role in transitioning to sustainable energy systems. Leveraging the work developed in the ENPOWER and Crete Valley projects, this research focuses on the Dingle Peninsula, where smart meter data is used to characterize energy behaviour and identify opportunities for load shifting and optimizing renewable energy use. Firstly, two interlinked Data-Sharing Workshops were conducted with local representatives and community members. Participants highlighted the importance of financial benefits, community independence from major energy providers, and contributions to the local sustainability. Concerns about data handling and privacy were also raised. Secondly, towards the energy behaviour characterization of the local community, historical active power measurements from 49 household smart meters, covering various periods between 2019 and 2022, were analysed. Households were categorized based on their behind-the-meter assets. Also, Monte Carlo simulations were applied to model electric vehicle (EV) charging patterns, accounting for variability and uncertainty, by using the data collected through the EV infrastructure available in Dingle.

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Date: 2025

Publication type: Journal article

Author(s): Vasilis Michalakopoulos, Ioannis Papias, Efstathios Sarantinopoulos , Elissaios Sarmas, Vangelis Marinakis, Dimitris Askounis

Abstract: Clustering residential electricity consumption patterns is a crucial step toward scalable and interpretable energy forecasting. Traditionally, clustering relies on direct analysis of load time series, which may obscure deeper behavioral or structural similarities between households. This study introduces a novel approach that shifts the focus from using raw consumption data to using the hyperparameters of stacked hour-ahead deep learning forecasting models—specifically based on LSTM, Bi-LSTM, and GRU architectures. By optimizing models independently for each household and clustering based on the resulting hyperparameter configurations, we reveal functional similarities in forecasting behavior that are not always evident in the original data. This method enables the formation of new, interpretable consumer segments, which can support the development of tailored forecasting models per cluster. Utilizing over three years of data from 200 households located in London, we evaluate the proposed hyperparameter-based clustering against traditional time-series clustering, applying standard metrics and explainable AI techniques (SHAP and LIME) to interpret the results. Findings demonstrate a strong alignment between the mean and median consumption patterns of clusters for both approaches, validating the effectiveness of the proposed method. Moreover, the analysis highlights that the feature transformation layers play the most critical role in shaping cluster formation, underscoring its significance in capturing underlying consumption behaviors. Beyond its analytical value, the method also offers a privacy-preserving advantage by requiring only the exchange of model hyperparameters rather than raw consumption data.

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