• Energy Research
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Energy efficiency is an increasingly important dimension of household appliances, which is why they are labeled to indicate their energy consumption. In 2020, the European Union countries changed the labeling system from the previous system: ranging from A+++ to D, to the new system: ranging from A to G, assuming it would be more transparent for the consumer. The aim of the study was to find out the extent to which consumers are aware of the new labeling system, and the impact that the new labels have (compared to the previous ones) on the perception of household appliances and consumer decision-making. For this purpose, the survey was conducted on a nationwide representative Polish sample (n = 1054). The research was partly experimental, where the same appliances were presented with new and previous energy labels. The results showed that currently most people do not identify the new energy classes. Furthermore, products with the new labels are perceived as being less energy efficient in comparison with products with the previous labels, which shows that there is some confusion among consumers in terms of the new energy efficiency labeling system.

The 2030 zero-net emission target in the E.U. demands a significant improvement in the energy performance of the building stock. This study analyses the adoption of connected thermostats and Home energy-management system solutions (HEMS) as an effective means to tackle the residential energy footprint. It reviews the main features of HEMS systems in terms of technology, cross-study performances, and the obstacles to widespread adoption; the study adopts the case-study methodology to examine the impact on the Italian real estate stock at a regional level. A matrix of adoption scenarios assesses the potential benefits of global residential energy savings, weighted by local climatic variations, dimension, number of single dwellings, and average primary energy reduction per household. Results demonstrate that all adoption scenarios dramatically reduce residential energy consumption, outperforming the E.U. targets for Italy by 2030.

The built environment remains a strategic research and innovation domain in view of the goal of full decarbonization. The priority is the retrofitting of existing buildings as zero-emission to improve their energy efficiency with renewable energy technologies pulling the market with cost-effective strategies. From the first age of photovoltaics (PV) mainly integrated in solar roofs, we rapidly moved towards complete active building skins where all the architectural surfaces are photoactive (Building Integrated Photovoltaics - BIPV). This change of paradigm, where PV replaces a conventional building material, shifted the attention to relate construction choices with energy and cost effectiveness. However, systematic investigations which put into action a cross-disciplinary approach between construction, economic and energy related domains is still missing. This paper provides the detailed assessment of a real multifamily building, taking into account retrofit scenarios for making active the building skin, with the goal to identify the sensitive aspects of the energetic and economic effectiveness of BIPV design options. By assuming a real case study with monitored data, the analysis will consider a breakdown of the main individual parts composing the building envelope, by then combining alternative re-configurations in merged clusters with different energy and construction goals. Results will highlight the correlation between building skin construction strategies and the energy and cost parameters by identifying the cornerstones for enhancing efficiency. The outcomes, related to the total life cost, self-consumption/sufficiency, in combination with different building design options (façade, roof, balconies, surface orientations, etc.), provide a practical insight for researchers and professionals to identify renovation strategies by synergistically exploiting the solar active parts towards lower global costs and higher energy efficiency of the whole building system.

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

Nuclear power can replace fossil fuels and will have a decisive impact on the change in the approach to conventional energy. However, nuclear (or radioactive) wastes are produced by the operation of the nuclear reactors should be safely and properly disposed of. This paper assesses the uranium resources and the global state of nuclear power plants and determines the energy mixes in different countries using the most nuclear energy. Furthermore, this paper analysed the nuclear waste management and disposal and the depletion of abiotic resources, and the primary energy sources of a basic production process using electricity mix and nuclear electricity for a basic production (PET bottle manufacturing) process. The life cycle assessment was completed by applying the GaBi 8.0 (version 10.6) software and the CML method. In this study, we limit our discussion to high-level nuclear waste (HLW) and spent nuclear fuel (SNF) waste. We do not consider waste generated from uranium mining and milling, which is usually disposed of in near-surface impoundments close to the mine or the mill. The investigation of waste management methods is limited to European countries. This research work is relevant because determining abiotic resources is important in a life cycle assessment and current literature available on LCA analysis for nuclear powers remains under-developed. These results can guide and compare manufacturing processes involving a nuclear electricity and electricity grid mix input. The results of this research can be used to develop production processes using nuclear energy with lower abiotic depletion impacts. This research work facilitates the industry in making predictions for a production-scale plant using an LCA of production processes with nuclear energy consumption.