Group Energy Conservation

How smart are European buildings? Case studies in Greece, published in Science and Technology for the Built Environment, 1–19. https://doi.org/10.1080/23744731.2026.2659353
The smart readiness indicator (SRI) is a new common European scheme for rating a building and assessing its capacity to accommodate smart-ready services. The primary goal is to secure the proper indoor environmental quality in accordance to the occupant needs, with low energy use, utilizing control strategies with building domains including HVAC, domestic hot water, lighting, envelope components, use of electricity, electric vehicle charging, monitoring and control. The paper presents an overview of the structure and outlines the SRI calculation steps and the generic technical framework. It provides a synopsis of the default catalogues that include smart-ready services for existing buildings with simple installations and new buildings with more complex installations. Furthermore, it summarizes the overall process to aggregate the calculations into a single SRI score. Following the available method and tool, several field studies have been performed in Hellenic commercial buildings. The results are elaborated to reveal the relation of the buildings’ energy performance and SRI in the existing condition and the resulting improvements following different renovation measures. The main findings reveal that the overall assessment method and tool are easy to implement. However, the user needs some expertise to interpret the building characteristics and features of its technical installations to the prescribed different levels. The initial SRI scores for the investigated case studies ranged from only 4.1% with an overall low energy performance (ranked at energy class-D), up to 42.9% with a high energy performance (ranked at energy class-B+). The assessment of various renovation scenarios for improving the building controls increased the SRI to reach a score of 24.8% (from 4.1%) up to 68.1% (from 35.6%). The associated energy savings ranged from 12% to 18%, respectively.

From Physics to Machine Learning in Forecasting Thermal Load of a District-Heated Residential Building: Assessment of Different Methods And Influence of Input Data, published in Energy & Buildings, 361: 117435, is now available as Open Access
https://doi.org/10.1016/j.enbuild.2026.117435
Accurate thermal load forecasting is crucial for optimizing the operation of heating systems, particularly under predictive control strategies. The choice of the forecasting method is strongly influenced by data availability and compatibility with control logic. However, there is still a lack in scientific literature on the detailed evaluation of forecast methods for the operation of buildings in weather-based control mode and in forecast control mode of building heating system. This study presents a structured multi-method approach to model space heating demand of a residential building in Poland, which has been monitored since 2018 and is being operated in forecasting mode since 2021 through the forHEAT system developed by the Lublin University of Technology. The analysis is articulated in three methods. Method 1 uses a calibrated EnergyPlus model to simulate building behavior under both weather-based (2018–2021) and forecasting-based control (2021–2024), offering insights into seasonal load patterns and about the limitations of static modeling under dynamic control. Method 2 develops 26 nonlinear autoregressive neural networks with exogenous inputs (NARX), exploring combinations of seasonal subsets, training durations, and control paradigms. Results show that recent, seasonally aligned training data enable the best performance (R2 = 0.937, cvRMSE = 9.96%), while shorter or misaligned datasets lead to higher error and reduced generalization. Method 3 introduces a simplified empirical model based on equivalent outdoor temperature (Teq) for real-time implementation. While Teq maintains low bias (NMBE within ± 0.6%), it suffers from higher variability (cvRMSE up to 27%) and limited adaptability. Overall, NARX networks consistently outperform both EnergyPlus and Teq across metrics, particularly in scenarios with transitional or mismatched regimes, making them the most robust tool for accurate heat load forecasting. Future developments can expand the model to other building typologies and climatic locations.

ReMED: TOWARDS CLIMATE RESILIENT URBAN CITIES is put to the test in five pilot sites in Croatia, Greece, Italy, Malta and Spain. Learn more in the latest Newsletter https://remed.interreg-euro-med.eu/wp-content/uploads/sites/63/newsletters/remed-pilot-sites-newsletter-6.pdf?fbclid=IwY2xjawRC_npleHRuA2FlbQIxMABzcnRjBmFwcF9pZBAyMjIwMzkxNzg4MjAwODkyAAEekBfIOOCJ4OpnkyvMHPrlh_13egkqOBgkwQX7n7aCpvuz1zwHm9srhXe8Yr8_aem_B53C3BfyJH3eELnCsSHTfg

Our 6th Newsletter summarising the pilot site reports is now available on the ReMED InterregEuro-Med website along with the links to the reports. Keep following for more updates!
http://remed.interreg-euro-med.eu/wp-content/uploads/sites/63/newsletters/remed-pilot-sites-newsletter-6.pdf

Grad Crikvenica Ayuntamiento de Málaga Fundación CIEDES Comune di Genova - Genoa Municipality Eco-Gozo A Better Gozo Italia R&D - International Initiative for a Sustainable Built Environment Faculty for the Built Environment - University of Malta University of Malta Group Energy Conservation ΔΗΜΟΣ ΦΥΛΗΣ Municipality of Fyli Interreg Euro-MED Programme

Good news, but no matter what Energy Conservation is still the first step 👍

𝟒𝟕.𝟑% 𝐨𝐟 𝐄𝐔’𝐬 𝐞𝐥𝐞𝐜𝐭𝐫𝐢𝐜𝐢𝐭𝐲 𝐜𝐚𝐦𝐞 𝐟𝐫𝐨𝐦 𝐫𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 ⚡♻️

Among EU countries, highest shares of electricity from renewable sources in:

🇩🇰Denmark (92.4%, mostly wind)
🇦🇹Austria (83.1%, mostly hydro)
🇵🇹Portugal (82.9%, mostly hydro and wind)

Lowest in:

🇲🇹Malta (16.2%)
🇨🇿Czechia (16.6%)
🇸🇰Slovakia (17.8%)

Read more 👉 https://link.europa.eu/VPbXr8