Operating buildings at a hierarchical level enhances grid flexibility by leveraging the diverse behavior of individual buildings. While Model Predictive Control (MPC) excels at local control of buildings using physics-based models, Reinforcement Learning (RL) offers data-driven adaptability for coordinating systems where detailed modeling is infeasible. This study proposes and evaluates a hierarchical RL-MPC framework given the advantages of each approach for managing energy systems across a cluster of buildings. This framework proposes a learning-based MPC at the decentralized building level to optimize HVAC operations (i.e., regulating indoor air temperature setpoints) based on a local system identification process via a Long-Short-Term Memory (LSTM) model. At the centralized district level, RL control (RLC) is designed to coordinate shared energy resources using thermal and electrical storage systems. The overall hierarchical control framework is implemented in the CityLearn environment, an open-source Gymnasium framework. To meet use-case needs, CityLearn is extended by incorporating additional energy system models, including a Fresnel solar thermal collector, thermal buffer storage, and an absorption chiller. The proposed hierarchical control framework is validated by real-world data from a cluster of buildings in Cyprus and evaluated under both typical and extreme weather conditions. The extreme weather condition is selected according to the MeteoSwiss definition, representing a heatwave period in Cyprus in July 2024. Performance is assessed using key performance indicators (KPIs), including solar self-consumption, ramping, daily peak demand, energy cost, and thermal discomfort. Results show that the proposed hierarchical RL-MPC approach consistently outperforms standalone RLC, particularly in stressed scenarios. Specifically, the hierarchical RL-MPC has achieved improvements in solar self-consumption of 9%, energy cost reduction of 7%, and enhanced thermal discomfort of 20% under typical weather conditions. Under extreme weather conditions, the KPIs improvements are 18%, 17%, and 5%, respectively. These findings highlight the benefits of integrating model-based and data-based control strategies within a hierarchical architecture to improve energy flexibility, efficiency, and resilience in smart energy communities.
To promote climate adaptation and mitigation strategies, it is crucial to understand the perspectives and knowledge gaps of stakeholders involved in functions affected by or addressing land use and climate changes. A large number of stakeholders across 21 European islands were consulted regarding their views on climate change and land use change issues affecting ecosystem services on their island. Climate change characteristics perceptions included variables such as temperature, precipitation, humidity, extremes, and wind. Land use change characteristics perceptions included deforestation, coastal degradation, habitat protection, renewable energy facilities, wetlands and other variables. Other environmental and societal problem perceptions such as invasive species, water or energy scarcity, problems in infrastructures or austerity were also included. Climate and land use change impact perceptions were analysed with machine learning to quantify their importance on the perception outcome. For example if a stakeholder perceives that pollution, coastal degradation, deforestation, precipitation decrease, and increase of humidity are occurring on the island, and austerity is the biggest problem how likely is that the impact of climate change or land use change will be quantified by the stakeholder as negative, unclear, neutral, or positive? The predominant climatic change characteristic is related with temperature, and the predominant land use change characteristic with deforestation. Water-related problems are top priorities for stakeholders. Energy-related problems, such as energy deficiency but also wind and solar energy facilities problems, rank high as combined climate change and land use change risks. Stakeholders generally perceive climate change impacts on ecosystem services as negative, with natural habitat destruction and biodiversity loss identified as the top variables. Land use change impacts are also negative but also more complex to explain, with a higher number of explanatory variables associated with the impact outcome. Stakeholders have common perceptions regarding climate change and land use change impacts on the benefits of biodiversity despite the geographic disparity. Stakeholders differentiate between factors related to climate change impacts and land use change impacts. Water, energy, and renewable energy related issues pose serious concerns to island stakeholders and management measures are needed to address them.
Solar photovoltaic (PV) systems play a crucial role in the global green energy transition, but their material requirements present challenges in terms of supply chain resilience. The required materials include concrete, steel, silver, cadmium, tellurium, indium, and selenium, as well as a range of other materials (aluminium, copper, silicon, germanium and gallium) that are considered either critical or strategically important due to their supply risks and economic importance. Although the challenges for PV systems are addressed in the ambitious European Green Deal of the European Union (EU) through circular economy strategies, implementation at the national level faces obstacles. To analyse the material implications of a green energy transition dominated by solar PVs, this paper focuses on Cyprus, an island country with a high solar energy potential and ambitious PV deployment targets. We use Material Flow Analysis to examine the retrospective and prospective material accumulations and trends under three scenarios: Business-As-Usual (BAU) that reflects a continuation of historical trends; With Existing Measures (WEM) which incorporates currently adopted and legislated policies; and Net-Zero Scenario (NZS) that targets full climate neutrality by 2050. The results show a substantial increase in material stocks across all scenarios, with the NZS projecting the most-significant growth, followed by the WEM and BAU. The NZS also demonstrates a more balanced evolution of material demand over time, potentially mitigating supply chain risks. If circular economy practices are effectively implemented, it is possible for aluminium, copper, silicon, and germanium to meet future material needs through recycling of materials recovered from decommissioned PV systems in Cyprus towards year 2050. We emphasize the importance of policy interventions to initiate waste management activities and to promote circularity in the PV industry, potentially through collaborations with recycling initiatives and other EU countries. Our findings highlight the need for strategic planning and a balanced approach to PV deployment, so as to ensure a resilient and sustainable energy transition for Cyprus, while emphasizing the potential of the NZS for achieving these goals.
Volatile organic compounds (VOCs) are key precursors of tropospheric ozone and secondary organic aerosol formation, yet multi-year observations in the Eastern Mediterranean and Middle East (EMME) remain limited. This study presents multi-year (April 2022–June 2024) high-resolution measurements of 76 VOCs using PTR-ToF-MS at a rural background site in Cyprus, combined with HYSPLIT air-mass analysis to examine the effects of regional transport on VOCs variability. Oxygenated VOCs (OVOCs) dominated the VOCs burden (∼ 79 %), followed by aliphatic hydrocarbons, aromatic hydrocarbons, and terpenes. Most VOCs exhibited clear diurnal patterns, observed highest during 08:00–14:00 UTC, varying by species, due to enhanced photochemical activity and temperature-driven emissions. Terpenes, particularly isoprene, increased exponentially with temperature upto 35–38 °C but decreased beyond this threshold, indicating heat-stress inhibition. Monoterpenes showed elevated levels both day and night, reflecting contributions from both biogenic and anthropogenic sources. OVOCs, including acetone, acetaldehyde, methanol, and acetic acid, showed sharp enhancement above 35 °C, consistent with intensified primary emissions and secondary formation under extreme heat. Aromatic hydrocarbons were mainly higher during winter, linked to combustion processes, but benzene levels were highest during summer particularly when temperature rose above 35 °C from evaporative and potential stress-related biogenic sources. HYSPLIT air-mass trajectory analysis revealed dominant contributions from Europe and Northwest Asia (∼ 68 %), transporting aged OVOCs, while Middle East winter inflows enhanced aromatic hydrocarbons. While WRF-Chem captured seasonal trends, most VOCs were underestimated, highlighting under-representation of emission sources and oxidation pathways in the model. Overall, the study emphasizes temperature and regional transport as key drivers of VOC variability in the Eastern Mediterranean.
The growing complexity of urban energy systems, climate uncertainties, and geopolitical disruptions highlight the need for energy flexibility and smart management. Recent developments in smart buildings enable real-time adaptability and collective energy behavior through the deployment of Reinforcement Learning (RL), which optimizes energy use, integrates distributed resources, and enhances demand response. However, challenges in communication, system diversity, and user intervention must be addressed for scalable and secure multi-agent RL-based management. This study evaluates the application of CIRLEM, a previously developed and introduced Energy Management system that integrates Collective Intelligence (CI) with an online, value-based, modelfree RL algorithm. The experiment is carried out in Building Energy Living Lab in France, as one of the pilots of COLLECTiEF, an European funded Horizon 2020 project, equipped with an advanced building management system for one year. The control algorithm interacts with the building management system every 15 min, optimizing setpoints based on real-time monitoring of energy use and indoor environmental conditions. The results indicate an 18% reduction in overall energy use compared to the reference baseline, with heating and cooling demands decreasing by 5% and 32%, respectively. Additionally, peak power demand is curtailed up to 15% for heating and 50% for cooling. The performance of the control algorithm is in an excellent level for more than 50% of the time in 1-month analyses through achieving load reduction and shifting. This experimental study demonstrates that CIRLEM effectively enhances energy flexibility while maintaining thermal comfort, demonstrating its potential for broader implementation, paving the way decentralized energy management solutions in smart buildings and urban energy networks.
