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The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

According to the European Energy Efficiency Directive for Buildings, member states are required to develop long-term strategies to adopt more sustainable, secure, and decarbonized energy systems in buildings by 2050. In this line of approach, an optional common regime has been established to define and calculate the smart readiness of buildings and assess their ability to adapt their operation to the needs of the occupants and the network. Thus, the smart readiness indicator (SRI) emerged, which assesses technological readiness by examining the presence and evaluation of the functionality level of various smart services, aiming at energy savings, the ability of the building to respond to users' needs, and energy flexibility. This paper focuses on examining the SRI calculation methodology's application to an office building, which is currently being deeply renovated. Initially, there is an analysis of the SRI, its calculation methodology, and its goals. This is followed by the practical calculation part of the SRI for a typical office building located in Greece and belonging to the climate zone of southern Europe. The results indicate that the SRIs application is not a straightforward issue since parameters that need to be considered are not regulated to the same degree. On the other hand, SRI can provide a stimulus for exploiting the renovation potential of buildings, precisely by integrating the various aspects and linking those to the use of innovative technologies. [ABSTRACT FROM AUTHOR]

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings' floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called "typology approach" for conducting public buildings' energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings' stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions' reduction. [ABSTRACT FROM AUTHOR]

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings. [ABSTRACT FROM AUTHOR]

The European Union faces the pressing challenge of decarbonising the buildings sector to meet its climate neutrality goal by 2050. Buildings are significant contributors to greenhouse gas emissions, primarily through energy consumption for heating and cooling. This study uses the advanced PRIMES-BuiMo model to develop state-of-the-art innovative pathways and strategies to decarbonise the EU buildings sector, providing insights into energy consumption patterns, renovation rates and equipment replacement dynamics in the EU and in two representative Member States, Sweden and Greece. The model-based analysis shows that the EU's transition towards climate neutrality requires significant investment in energy efficiency of buildings combined with decarbonisation of the fuel mix, mostly through the uptake of electric heat pumps replacing the use of fossil fuels. The Use Case also demonstrates that targeted policy interventions considering the national context and specificities are required to ensure an efficient and sustainable transition to zero-emission buildings. The analysis of transformational strategies in Greece and Sweden provides an improved understanding of the role of country-specific characteristics on policy effectiveness so as to inform more targeted and contextually appropriate approaches to decarbonise the buildings sector across the EU. [ABSTRACT FROM AUTHOR]

Climate change is expected to significantly affect countries in Southern Europe and the Mediterranean Basin, causing higher-than-average temperature increases, considerable reductions in rainfall and water runoff, and extreme events such as heatwaves. These pose severe threats to local energy systems, requiring a reliable and quantitative risk analysis. A methodological approach is thus proposed which covers both energy supply and demand, utilizing the latest climate projections under different greenhouse gas emissions scenarios and an appropriate scale for each energy form. For energy supply technologies, risks are assessed through statistical regression models and/or mathematical equations correlating climatic parameters with energy productivity. To analyze climate risks for energy demand, bottom–up models were developed, integrating both behavioral and policy aspects which are often considered in a very limited way. The results show that climate change will mainly affect electricity generation from hydroelectric and thermal power plants, with variations depending on the plants' locations and uncertainties associated with precipitation and runoff changes. The climate risks for solar and wind energy were found to be low. Energy consumption will also be affected, but the range of risks depends on the ambition and the effectiveness of measures for upgrading the thermal performance of buildings and the intensity of climate change. [ABSTRACT FROM AUTHOR]

• A new energy balance model of outdoor swimming pools assesses their heating demand. • The proposed model was validated using the experimental outdoor data set. • Lowing the operating water temperature by 1 °C cuts energy consumption by about 11 % • Anti-evaporative cover usage reduces energy demand, with up to 30% savings. • Local wind conditions play a key role in outdoor pool energy consumption. In the Mediterranean region, outdoor swimming pools, despite having lower energy consumption compared to indoor pools, are nonetheless highly energy-intensive structures, offering significant opportunities for energy efficiency improvements. Although there are numerous studies in the literature, few of these works have been dedicated to estimating the energy consumption of outdoor pools while considering typical local meteorological conditions and their occupancy rates. This paper presents a novel energy balance model for outdoor pools that incorporates the latest correlations for calculating evaporation due to forced convection and sky temperature, a new phenomenological model for assessing the enhancement of evaporation as a function of occupancy rate, and an approach that takes into account atmospheric stability conditions in defining wind-related heat losses. The model successfully predicts the annual energy consumption data for outdoor Olympic swimming pools in Greece with a mean absolute percentage error of less than 12 %. Simulations of an outdoor swimming pool operating with water at 26.5 °C and in which the cover is not used present indicate a specific thermal energy consumption of about 2300 kWh/m2. A decrease in water temperature by 1 °C and the use of a cover result in approximately 11 % and 30 % reduction in consumption, respectively. [ABSTRACT FROM AUTHOR]