• Energy Research
• 2025-2025close
• engineering and technology close
• FR close
• PL close
• AT close

Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater cost savings. Energy Peer-to-Peer (P2P) systems have thus been shown to be beneficial for prosumers and consumers through reductions in energy cost while also being attractive to grid or service providers. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities; such challenges include the inherent complexity of matching together geographically distributed peers and the significant computations required to calculate the local matching preferences. Hence dedicated algorithms are required to be able to perform a cost-efficient matching of thousands of peers in a computational-efficient fashion. We define and analyze in this work a precise mathematical modelling of the geographical peer matching problem and several heuristics solving it. Our experimental study, based on real-world energy data, demonstrates that our solutions are efficient both in terms of cost savings achieved by the peers and in terms of communication and computing requirements. Our scalable algorithms thus provide one core building block for practical and data-efficient peer-to-peer energy sharing communities within large-scale optimization systems.

Comprehensive occupancy information in smart buildings has become more imperative in order to develop new control strategies in energy management systems. Several techniques can be used to collect occupancy information considering accurate sensing techniques, such as passive infrared (PIR), carbon dioxide (CO2) and different types of cameras (i.e., thermal, or optical cameras). Recent studies show the usefulness of integrating occupancy information into energy management systems to reduce energy consumption while maintaining the occupants’ comfort. The purpose of this work is to elaborate a comprehensive review on occupancy detection systems in smart buildings. This study presents a set of comparison standards including methods, occupancy resolution, type of buildings and sensors. A classification of different approaches, which can be implemented and integrated into the building management system for detecting indoor occupancy, is introduced. Summary and discussions are given by highlighting the usefulness of machine learning for enabling predictive control of active systems in smart buildings.

Enhancing the performance of traditional vapor compression cooling cycles is an important aspect in the quest to minimize global energy consumption, to own sustainable energy systems soon, and to preserve the environment. This study performed a comparative analysis of the performance of a water cooler with different working fluids to replace R143a and improve system performance. A mathematical model derived from energy and exergy analysis is developed for the evaluation of the effect of operating conditions on the system COP, exergetic losses, and exergetic efficiency. The evaluation has been conducted for evaporation and condensation temperatures ranging between -30°C to 15°C and 25°C to 55°C, respectively. Results showed that the cycle with R510A has the maximum COP. The average system COP with R510A, RE170, and R152a are 19.54%, 13.53%, and 9.36 % higher than that with R134a, respectively. The highest value of exergy loss takes place in the compressor. At different working fluids, exergy losses decrease as evaporation temperatures increase and condensation temperatures decrease. The system with R510A has the minimum exergy losses. The average exergy losses for systems with R510A, RE170, and R152a are 34.62%, 28.33%, and 18.64% lower than that of R134a, respectively. The system with R510A has higher exergy efficiency and R134a has the minimum values of exergy efficiency. Generally, the water cooler provided better performance with R510A and RE170 than with R152a and R134a. Therefore, R510A can be considered as the best replacement for R134a and R152a.

International audience

Throughout Europe, large housing estates dating from the socialist era are an important part of the housing stock and cultural life, and in some cities the apartment buildings within them house more than half of the residents. There have been many attempts to regenerate large housing estates, since they were built with cheap materials, using contemporaneous (and outdated) planning principles, and were projected to be used for only one generation as ‘temporary housing’ until more permanent solutions could be established. Consequently, many large housing estates have survived beyond their useful life and are in need of renovation. This article moves beyond individual and disconnected rehabilitation projects and renewal strategies and conceptualises a larger transformation of large housing estates in Central and Eastern Europe based on sustainability principles. We envision a comprehensive ‘green transformation’ that would transform large housing estates into ‘eco-villages’ that not only address current challenges but also propel large housing estates into their next lives as desirable residential space. The image of housing estates can be vastly improved if the built environments and landscapes are transformed so that housing estates possess green infrastructure and other features of sustainable design. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licences/by-nc-nd/4.0/ .

Every day, we experience the effects of the global warming: extreme weather events, major forest fires, storms, global warming, etc.The scientific community acknowledges that this crisis is a consequence of human activities where Information and Communications Technologies (ICT) are an increasingly important contributor.Computer scientists need tools for measuring the footprint of the code they produce and for optimizing it. Running Average Power Limit (RAPL) is a low-level interface designed by Intel that provides a measure of the energy consumption of a CPU (and more) without the need for additional hardware. Since 2017, it is available on most computing devices, including non-Intel devices such as AMD processors.More and more people are using RAPL for energy measurement, mostly like a black box without deep knowledge of its behavior.Unfortunately, this causes mistakes when implementing measurement tools.In this paper, we propose to come back to the basic mechanisms that allow to use RAPL measurements and present a critical analysis of their operations. In addition to long-established mechanisms, we explore the suitability of the recent eBPF technology (formerly and abbreviation for extended Berkeley Packet Filter) for working with RAPL.For each mechanism, we release an implementation in Rust that avoids the pitfalls we detected in existing tools, improving correctness, timing accuracy and performance. These new implementations have desirable properties for monitoring and profiling parallel applications.We also provide an experimental study with multiple benchmarks and processor models (Intel and AMD) in order to evaluate the efficiency of the various mechanisms and their impact on parallel software.These experiments show that no mechanism provides a significant performance advantage over the others. However, they differ significantly in terms of ease-of-use and resiliency.We believe that this work will help the community to develop correct, resilient and lightweight measurement tools.

International audience