Assessment of energetic, economic and environmental performance of ground-coupled heat pumps

Ground-coupled heat pumps (GCHPs) have a great potential for reducing the cost and climate change impact of building heating, cooling, and domestic hot water (DHW). The high installation cost is a major barrier to their diffusion but, under certain conditions (climate, building use, alternative fuels, etc.), the investment can be profitable in the long term. We present a comprehensive modeling study on GCHPs, performed with the dynamic energy simulation software TRNSYS, reproducing the operating conditions of three building types (residential, office, and hotel), with two insulation levels of the building envelope (poor/good), with the climate conditions of six European cities. Simulation results highlight the driving variables for heating/cooling peak loads and yearly demand, which are the input to assess economic performance and environmental benefits of GCHPs. We found that, in Italy, GCHPs are able to reduce CO2 emissions up to 216 g CO2/year per euro spent. However, payback times are still quite high, i.e., from 8 to 20 years. This performance can be improved by changing taxation on gas and electricity and using hybrid systems, adding a fossil-fuel boiler to cover peak heating loads, thus reducing the overall installation cost compared to full-load sized GCHP systems

Landscape of solutions in constraint satisfaction problems

We present a theoretical framework for characterizing the geometrical properties of the space of solutions in constraint satisfaction problems, together with practical algorithms for studying this structure on particular instances. We apply our method to the coloring problem, for which we obtain the total number of solutions and analyze in detail the distribution of distances between solutions.Comment: 4 pages, 4 figures. Replaced with published versio

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Assessment of Energetic, Economic and Environmental Performance of Ground-Coupled Heat Pumps

Ground-coupled heat pumps (GCHPs) have a great potential for reducing the cost and climate change impact of building heating, cooling, and domestic hot water (DHW). The high installation cost is a major barrier to their diffusion but, under certain conditions (climate, building use, alternative fuels, etc.), the investment can be profitable in the long term. We present a comprehensive modeling study on GCHPs, performed with the dynamic energy simulation software TRNSYS, reproducing the operating conditions of three building types (residential, office, and hotel), with two insulation levels of the building envelope (poor/good), with the climate conditions of six European cities. Simulation results highlight the driving variables for heating/cooling peak loads and yearly demand, which are the input to assess economic performance and environmental benefits of GCHPs. We found that, in Italy, GCHPs are able to reduce CO2 emissions up to 216 g CO2/year per euro spent. However, payback times are still quite high, i.e., from 8 to 20 years. This performance can be improved by changing taxation on gas and electricity and using hybrid systems, adding a fossil-fuel boiler to cover peak heating loads, thus reducing the overall installation cost compared to full-load sized GCHP systems

Landscape of solutions in constraint satisfaction problems

4 pages, 4 figures. Replaced with published versionWe present a theoretical framework for characterizing the geometrical properties of the space of solutions in constraint satisfaction problems, together with practical algorithms for studying this structure on particular instances. We apply our method to the coloring problem, for which we obtain the total number of solutions and analyze in detail the distribution of distances between solutions

Landscape of Solutions in Constraint Satisfaction Problems

We present a theoretical framework for characterizing the geometrical properties of the space of solutions in constraint satisfaction problems, together with practical algorithms for studying this structure on particular instances. We apply our method to the coloring problem, for which we obtain the total number of solutions and analyze in detail the distribution of distances between solutions

Dynamic simulation of Ground-Coupled Heat Pumps (GCHPs): insights on the economic convenience and on the environmental benefits

Ground-Coupled Heat Pumps (GCHPs) allow to achieve noticeable reduction of primary energy consumption, of CO2 emissions, and operational costs of Heating, Ventilation and Air Cooling (HVAC) systems. The large installation expense is a strong barrier to their diffusion; however, GCHPs can be economically convenient in a number of contexts. We present a study which identifies such cases, based on integrated building-HVAC-GCHP dynamic simulations carried out with the software TRNSYS. Three building types were simulated, with very different schedules - a detached house, a hotel, and an office building – considering, for each of them, the case of a highly-insulated and a lowly-insulated envelope by adopting, respectively, the transmittance values typical of brand-new and Sixties’ buildings. To take into account climate conditions, 6 locations were chosen ranging from warm Mediterranean (Seville) to cold Scandinavian climate (Stockholm). The results of simulation allow to identify key parameters which influence the economic viability of GCHP systems, such as peak power demand and full-load equivalent operating hours. As shown in the results, geothermal heat pumps covering the whole heating demand are almost unfeasible without public subsidies, since the reduction of operational cost is not sufficient to cover the difference in the initial expense compared to a gas boiler. An exception is represented by hotels, due to their intense use of the heating system. To reduce the initial investment, hybrid heat pump-gas boiler configurations can be adopted, where the HP is installed to cover the base demand and a backup gas boiler is used to cover peaks. In this way, a cheaper heat pump can be installed, although covering a large share of the heating demand with renewable heat from the ground. The ratio between electricity price and fuel price is another driving parameter, since it influences the saving margin and hence the payback time of installing a heat pump. Payback times can dramatically be decreased acting on the electricity price, e.g. reducing taxes. Finally, we analysed the effectiveness of incentives granted for different energy refurbishment interventions to avoid CO2 emissions, finding that GCHP (and, in particular, hybrid GCHP) are much more effective than interventions on the building envelope, such as window replacement

Water-energy nexus in shallow geothermal systems

Ground-source heat pumps (GSHPs) reduce CO2 emissions compared to conventional heating and cooling systems. The thermally altered zone (thermal plume) is a key aspect for land management of GSHPs