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Finland has approximately 150,000 oil-heated private homes. In 2020, the Finnish government launched subsidies for private homeowner energy renovations. In this study, we examine the impact of two new energy renovation subsidies, the ELY grant and the ARA grant, from an energy efficiency point of view. Data from these subsidies reveal that a typical energy renovation case is a building from the 1970s where the oil boiler is replaced with an air-to-water heat pump. With additional data from the Finnish Energy certificate registry, a reference 1970s house is constructed and modelled in the building simulation programme, IDA ICE 4.8. Combinations of several renovation measures are simulated: air-to-water heat pump, ground-source heat pump, ventilation heat recovery and improved insulation. We found that resorting mainly to air-to-water heat pumps is not the most energy-effective solution. Ground-source heat pumps deliver a more significant reduction in delivered energy, especially with additional measures on insulation and heat recovery. Ground-source heat pumps also demand slightly less power than air-to-water heat pumps. Onsite solar PV generation helps supplement part of the power needed for heat pump solutions. Subsidy policies should emphasize deep renovation, ventilation heat recovery and onsite electricity generation.

| openaire: EC/H2020/754177/EU//NERO The study addressed the improvement of building energy performance along with a good thermal comfort and indoor air quality (IAQ) in newly built Finnish daycare and school buildings. Indoor Environmental Quality (IEQ), calculated and actual energy performance, investment and life cycle cost (LCC) were monitored. Ventilation airflow rates, temperature, and CO2 were measured onsite in five daycare and school buildings during summer and winter, and an occupant questionnaire survey was conducted in parallel with onsite measurements. Energy performance was evaluated based on an energy performance certificate (EPC) and actual metered consumption to figure out possible performance gaps. Simulations were conducted to calibrate models and analyse improvements in ventilation system operation, which showed energy-saving potential while ensuring good thermal comfort and IAQ. The thermal comfort and IEQ of all buildings were excellent and were in line with the results of the questionaries survey except in 'School 3', which had specific odour and noise problems. The measured energy use of all buildings except 'School 3' was increased by a factor of 2.1-2.6 compared to the EPC value, caused by poor control of a ventilation system and the presence of some non-regulated uses such as hot kitchens, washing machines, and dryers. LCC considerably depended on actual energy use stressing the importance of accurate energy prediction. It was shown that the reduction of energy use by a factor of 1.9-3.1 is possible without compromising indoor climate by adjustment of correct operation hours, improving ventilation system control, and limiting excessive outdoor airflows in some cases. Peer reviewed

| openaire: EC/H2020/856602/EU//FINEST TWINS Funding Information: This work has been supported by the European Commission through the H2020 project Finest Twins grant No. 856602; and by the Estonian Ministry of Education and Research and European Regional Fund grant 2014-2020.4.01.20-0289. Additional support was acquired from the Estonian Research Council grant PSG739. Publisher Copyright: © 2023 Increasing use of volatile renewable energy sources causes challenges in balancing supply and demand. Therefore, demand-side flexibility has rising importance for system operators and balancing authorities. Flexibility management methods are needed to integrate loads like ventilation systems of different buildings (e.g., residential and commercial) into flexibility service. However, the available methods described in research papers require further development for implementation in practice. Heating and cooling systems have received much attention from researchers, but the potential of ventilation systems has been left out of focus. Therefore, this paper provides a complete set of novel flexibility management methods for ventilation systems created from an aggregator's viewpoint. The flexibility is quantified through capacity (e.i. the amount of power consumption that can be altered), forced ventilation rate duration, and the tendered price for the service. The proposed methods were tested on a building model constructed and simulated in IDA ICE. The data processing and flexibility management methods were applied in MATLAB. Two types of ventilation systems with different sensor configurations were considered: constant and variable air volume. Forced ventilation rate duration is calculated using energy and mass balance analysis where the root means squared error was 10 to 33 min, depending on the system type, measured parameter, and sensor location. The flexibility service pricing model was tested on the 2022 years' manual frequency restoration reserve (mFRR) activation and balance energy market data. Peer reviewed

With the publication of the European Green Deal, the European Union has committed to reaching carbon neutrality by 2050. The envisaged reductions of direct greenhouse gases emissions are seen as technically feasible, but if a wrong path is pursued, significant unintended impacts across borders, sectors, societies and ecosystems may follow. Without the insights gained from an impact assessment framework reaching beyond the techno-economic perspective, the pursuit of direct emission reductions may lead to counterproductive outcomes in the long run. We discuss the opportunities and challenges related to the creation and use of an integrated assessment framework built to inform the European Commission on the path to decarbonisation. The framework is peculiar in that it goes beyond existing ones in its scope, depth and cross-scale coverage, by use of numerous specialised models and case studies. We find challenges of consistency that can be overcome by linking modelling tools iteratively in some cases, harmonising modelling assumptions in others, comparing model outputs in others. We find the highest added value of the framework in additional insights it provides on the technical feasibility of decarbonisation pathways, on vulnerability aspects and on unintended environmental and health impacts on national and sub-national scale.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract Swimming halls (SHs), which belong to special building sector, are easily overlooked as significant energy users and carbon producers. The demands of heat (e.g., pool water heating, domestic hot water, space and supply air heating) and electricity (e.g., Saunas, pool pumping and ventilation) in SHs are both very high, which indicates the high energy saving potentials. This paper comprehensively introduced and summarized the energy-saving potentials mainly for the heating system in SHs. After briefly introducing the global energy and building energy backgrounds, the energy system of SHs was firstly introduced, including its energy use and breakdown, ventilation demand, and heat losses. Then, renewable and sustainable energy sources applied in SHs were reviewed, especially solar energy application in terms of individual solar-assisted heating and hybrid solar-assisted heat pump systems, while geothermal and biomass and other energy applications were also introduced briefly. Furthermore, building energy management (BEM) strategies were introduced and summarized comprehensively, including waste heat utilization, prediction of energy demand and consumption, control and optimization of HVAC system, and demand response potentials of both electricity and district heat. In the discussion part, the recommendations of high-efficient or energy-saving technologies in SHs were given as well as the future development. Finally, suggestions were given for achieving energy efficiency and carbon reduction in SHs via using renewable energy sources (especially solar energy), optimizing the energy and HVAC systems, possible waste heat recoveries, and applying demand response of energy. In addition, establishing feasible and comprehensive energy indexes to evaluate energy use in SHs is also essential in the future.

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

AbstractClimate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights.