Keywords
Ground Source Heat Pumps; Spatial Decision Support System; Renewable Energy Sources; Regional Energy Planning
Start Date
26-6-2018 10:40 AM
End Date
26-6-2018 12:00 PM
Abstract
Reaching the ambitious European energy and climate goals asks the heating and cooling (H&C) sector for a drastic reduction in energy consumption and a significant increase of production from renewable energy sources (RES). Nowadays in Europe half of the energy consumption is used for H&C, and 84% of this energy is still generated from fossil fuels while only 16% is generated from RES. The Ground Source Heat Pump (GSHP) technology can play a strategic role, increasing the efficiency of H&C systems and strengthening the flexibility of the whole energy system. This study presents a Spatial Decision Support System (SDSS) designed to include the GSHP into the energy strategy and spatial planning process. The model integrates legal, technical, environmental, and economic constraints at the local/regional scale. The SDSS allows to compare scenarios with different technological solutions, also relating them to different energy, environmental and financial targets. The proposed SDSS is applied in a case study i.e. Valle d’Aosta, a small Italian alpine region. The spatial unit of analysis is the single building; where the information at the building level was not available, data from the Italian National Statistics Institute at the census tract scale has been integrated for filling the knowledge gap. The aim of this model is to support decision-makers in fostering sustainable energy plans targeted to improve both energy production from RES and energy renovation of the existing building stock. The research output can have a key impact in the H&C field helping to increase the knowledge of the GSHP technology and to enhance its integration into energy plans and strategies. In addition, it can contribute to reduce CO2 emissions in the energy production sector. Finally, the research aims to develop a broader approach towards the energy transition of cities and regions starting from local settings.
A SDSS to integrate ground source heat pump into regional energy strategies
Reaching the ambitious European energy and climate goals asks the heating and cooling (H&C) sector for a drastic reduction in energy consumption and a significant increase of production from renewable energy sources (RES). Nowadays in Europe half of the energy consumption is used for H&C, and 84% of this energy is still generated from fossil fuels while only 16% is generated from RES. The Ground Source Heat Pump (GSHP) technology can play a strategic role, increasing the efficiency of H&C systems and strengthening the flexibility of the whole energy system. This study presents a Spatial Decision Support System (SDSS) designed to include the GSHP into the energy strategy and spatial planning process. The model integrates legal, technical, environmental, and economic constraints at the local/regional scale. The SDSS allows to compare scenarios with different technological solutions, also relating them to different energy, environmental and financial targets. The proposed SDSS is applied in a case study i.e. Valle d’Aosta, a small Italian alpine region. The spatial unit of analysis is the single building; where the information at the building level was not available, data from the Italian National Statistics Institute at the census tract scale has been integrated for filling the knowledge gap. The aim of this model is to support decision-makers in fostering sustainable energy plans targeted to improve both energy production from RES and energy renovation of the existing building stock. The research output can have a key impact in the H&C field helping to increase the knowledge of the GSHP technology and to enhance its integration into energy plans and strategies. In addition, it can contribute to reduce CO2 emissions in the energy production sector. Finally, the research aims to develop a broader approach towards the energy transition of cities and regions starting from local settings.
Stream and Session
B2: Hybrid modelling and innovative data analysis for integrated environmental decision support