• Energy Research

The transition to low carbon energy systems poses challenges in terms of energy efficiency. In building refurbishment projects, efficient technologies such as smart controls and heat pumps are increasingly being used as a substitute for conventional technologies with the aim of reducing carbon emissions and determining operational energy and cost savings, together with other benefits. Measured building performance, however, often reveals a significant gap between the predicted energy use (design stage) and actual energy use (operation stage). For this reason, lean and interpretable digital twins are needed for building energy monitoring aimed at persistence of savings and continuous performance improvement. In this research, interpretable regression models are built with data at multiple temporal resolutions (monthly, daily and hourly) and seamlessly integrated with the goal of verifying the performance improvements due to Smart thermostatic radiator valves (TRVs) and gas absorption heat pumps (GAHPs) as well as giving insights on the performance of the building as a whole. Further, as part of modelling research, time of week and temperature (TOWT) approach is reformulated and benchmarked against its original implementation. The case study chosen is Hale Court sheltered housing, located in the city of Portsmouth (UK). This building has been used for the field-testing of innovative technologies such as TRVs and GAHPs within the EU Horizon 2020 project THERMOSS. The results obtained are used to illustrate possible extensions of the use of energy signature modelling, highlighting implications for energy management and innovative building technologies development.

The transition to low carbon energy systems poses challenges in terms of energy efficiency. In building refurbishment projects, efficient technologies such as smart controls and heat pumps are increasingly being used as a substitute for conventional technologies with the aim of reducing carbon emissions and determining operational energy and cost savings, together with other benefits. Measured building performance, however, often reveals a significant gap between the predicted energy use (design stage) and actual energy use (operation stage). For this reason, lean and interpretable digital twins are needed for building energy monitoring aimed at persistence of savings and continuous performance improvement. In this research, interpretable regression models are built with data at multiple temporal resolutions (monthly, daily and hourly) and seamlessly integrated with the goal of verifying the performance improvements due to Smart thermostatic radiator valves (TRVs) and gas absorption heat pumps (GAHPs) as well as giving insights on the performance of the building as a whole. Further, as part of modelling research, time of week and temperature (TOWT) approach is reformulated and benchmarked against its original implementation. The case study chosen is Hale Court sheltered housing, located in the city of Portsmouth (UK). This building has been used for the field-testing of innovative technologies such as TRVs and GAHPs within the EU Horizon 2020 project THERMOSS. The results obtained are used to illustrate possible extensions of the use of energy signature modelling, highlighting implications for energy management and innovative building technologies development.

Abstract The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle, is to improve building and system performances in terms of economics, comfort, environmental impact and durability. Several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in particular with respect to the cross-disciplinary role of data. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in modelling strategies. Visualization and data analysis techniques are successfully used in a wide variety of applications, both in theoretical and applied domains, but questions remains about their robustness, efficiency and applicability to the problems introduced before. The paper aims to analyze critically these topics by means of case studies, showing a possible path to create a multi-scale methodology able to synthesize all the relevant aspects.

Abstract The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle, is to improve building and system performances in terms of economics, comfort, environmental impact and durability. Several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in particular with respect to the cross-disciplinary role of data. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in modelling strategies. Visualization and data analysis techniques are successfully used in a wide variety of applications, both in theoretical and applied domains, but questions remains about their robustness, efficiency and applicability to the problems introduced before. The paper aims to analyze critically these topics by means of case studies, showing a possible path to create a multi-scale methodology able to synthesize all the relevant aspects.

Abstract Hydrogen is assuming a crescent role in the decarbonising initiatives. Moreover, hydrogen can supply the 3 most energy intense sectors, i.e. transport, heat and electricity, allowing the sector coupling. To do so, a production unit, the electrolyser, and a consumer one, the fuel cell, are needed. Actually, reversible Solid Oxide Cell technology presents the possibility to install only one device acting bi-directionally. It offers different advantages thank to its (i) compact design, thus ensuring space and cost savings; (ii) ability to meet thermal and electric demand, thus reducing emissions in both those sectors; (iii) possibility to store, the electricity excess coming from renewable sources in form of hydrogen, ensuring an unlimited and seasonal storage possibility. In this study, the deployment of a real reversible Solid Oxide Cell was simulated in different scenarios considering the data recorded in one year in the island of Procida, Italy. Up to date, the use of this technology was mostly relegated to the industrial sector or to prototype tests. While, this research aimed to analyse the functioning of near commercialization technology in civil environments such as hotels, offices and hospitals to understand its feasibility in this new context. It wants to be proved that advantages of this emerging technology can be exploited as well in the civil environment. Three economic indicators, i.e. Payback Period, Internal Return Rate and Net Present Value were selected to evaluate the simulated scenarios, while, the primary energy saving, the emission reduction and its storage efficacy were studied to evaluate the environmental achievements. To perform the simulations, the MATLAB model ConfigDym built by Sylfen was used. Finally, a sensitivity analysis in terms of economics was carried out. The results show an important decrease in emissions and an energy self-sufficiency increase of at least 29% and 58% respectively, differently the economic analysis returns a payback period currently near to its lifetime, while for the future a three years period is reachable.

Abstract Hydrogen is assuming a crescent role in the decarbonising initiatives. Moreover, hydrogen can supply the 3 most energy intense sectors, i.e. transport, heat and electricity, allowing the sector coupling. To do so, a production unit, the electrolyser, and a consumer one, the fuel cell, are needed. Actually, reversible Solid Oxide Cell technology presents the possibility to install only one device acting bi-directionally. It offers different advantages thank to its (i) compact design, thus ensuring space and cost savings; (ii) ability to meet thermal and electric demand, thus reducing emissions in both those sectors; (iii) possibility to store, the electricity excess coming from renewable sources in form of hydrogen, ensuring an unlimited and seasonal storage possibility. In this study, the deployment of a real reversible Solid Oxide Cell was simulated in different scenarios considering the data recorded in one year in the island of Procida, Italy. Up to date, the use of this technology was mostly relegated to the industrial sector or to prototype tests. While, this research aimed to analyse the functioning of near commercialization technology in civil environments such as hotels, offices and hospitals to understand its feasibility in this new context. It wants to be proved that advantages of this emerging technology can be exploited as well in the civil environment. Three economic indicators, i.e. Payback Period, Internal Return Rate and Net Present Value were selected to evaluate the simulated scenarios, while, the primary energy saving, the emission reduction and its storage efficacy were studied to evaluate the environmental achievements. To perform the simulations, the MATLAB model ConfigDym built by Sylfen was used. Finally, a sensitivity analysis in terms of economics was carried out. The results show an important decrease in emissions and an energy self-sufficiency increase of at least 29% and 58% respectively, differently the economic analysis returns a payback period currently near to its lifetime, while for the future a three years period is reachable.

Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0-28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7-26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.