Abstract
This paper aims to model collaborative behaviours related to distributed generation investments in a residential neighbourhood, by means of Cooperative Game Theory (CGT). The main objective is to analyse the economic impact of the installation of photovoltaic solar panels and batteries, and the assessment of the potential energy savings due to the cooperation among residential households. In this regard, both the purchase of needed energy from the grid and the sale of the spare energy from the households' self-generation to the grid are considered. The comparison between cooperative and non-cooperative behaviours in the investments and energy management decisions is analysed based on the Shapley value, a cost sharing method from CGT, concluding that cooperation among prosumers to share energy in a residential energy community is in general more efficient than prosumers making individual decisions.

Abstract
A novel system to monitor methane fugitive emissions was developed using passive optical sensors to attend to the natural gas production and transportation industry. The system is based on optical time domain reflectometry and direct optical absorption spectroscopy. The system was tested in a gas compressor station for four months. The system was capable to measure methane concentration at two points showing its correlation with meteorological data, specially wind velocity and local temperature. Methane concentrations varied from 2.5% to 15% in the first monitored point by sensor 1, and from 5% to 30%, in the second point with sensor 2. Both sensors exhibited a moderate negative correlation with wind velocity with a mean Pearson coefficient of -0.61, despite the external cap designed to avoid the influence of wind. Sensor 2 had a modification to its external package that reduced this mean correlation coefficient to -0.30, considered to be weak to negligible. Regarding temperature, a moderate mean correlation of -0.59 was verified for sensor 1 and zero mean correlation was found for sensor 2. Based on these results the system was proven to be robust for installation in gas transportation or processing facilities.

Abstract

Abstract
The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

Abstract
Iterative compilation focused on specialized phase orders (i.e., custom selections of compiler passes and orderings for each program or function) can significantly improve the performance of compiled code. However, phase ordering specialization typically needs to deal with large solution space. A previous approach, evaluated by targeting an x86 CPU, mitigates this issue by first using a training phase on reference codes to produce a small set of high-quality reusable phase orders. This approach then uses these phase orders to compile new codes, without any code analysis. In this paper, we evaluate the viability of using this approach to optimize the GPU execution performance of OpenCL kernels. In addition, we propose and evaluate the use of a heuristic to further reduce the number of evaluated phase orders, by comparing the speedups of the resulting binaries with those of the training phase for each phase order. This information is used to predict which untested phase order is most likely to produce good results (e.g., highest speedup). We performed our measurements using the PolyBench/GPU OpenCL benchmark suite on an NVIDIA Pascal GPU. Without heuristics, we can achieve a geomean execution speedup of 1.64x, using cross-validation, with 5 non-standard phase orders. With the heuristic, we can achieve the same speedup with only 3 non-standard phase orders. This is close to the geomean speedup achieved in our iterative compilation experiments exploring thousands of phase orders. Given the significant reduction in exploration time and other advantages of this approach, we believe that it is suitable for a wide range of compiler users concerned with performance.

Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers' consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers' usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers' elasticity is effectively utilized.