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The emerging energy transition is particularly described as a move towards a cleaner, lower-carbon system. In the context of the global shift towards sustainable energy sources, this paper reviews the potential and roadmap for hydrogen energy as a crucial component of the clean energy landscape. The primary objective is to present a comprehensive literature overview, illuminating key themes, trends, and research gaps in the scientific discourse concerning hydrogen production and energy policy. This review focuses particularly on specified geographic contexts, with an emphasis on understanding the unique energy policies related to renewable energy in Brazil, Austria, and Germany. Given their distinct social systems and developmental stages, this paper aims to delineate the nuanced approaches these countries adopt in their pursuit of renewable energy and the integration of hydrogen within their energy frameworks. Brazil exhibits vast renewable energy potential, particularly in wind and solar energy sectors, positioning itself for substantial growth in the coming years. Germany showcases a regulatory framework that promotes innovation and technological expansion, reflecting its highly developed social system and commitment to transitioning away from fossil fuels. Austria demonstrates dedication to decarbonization, particularly through the exploration of biomethane for residential heating and cooling. [ABSTRACT FROM AUTHOR]

Efforts towards climate neutrality in Europe must prioritise manufacturing industries, particularly the energy-intensive industry (EII) subsectors. This work proposes a novel approach to assessing transformation options for EII subsectors. At the center of this approach we position a potential analysis of technologies' impact on subsector decarbonisation—an approach only known so far from the investigation of renewable energy potentials. These so-called technical climate neutrality potentials, supplemented by a set of indicators taking into account energy consumption, capital and operational expenditures, and GHG taxation programs per technology and subsector, enable cross-sector comparisons. The indicators allow the reader to compare the impact on GHG emission mitigation, energy demand, and cost for every considered technology. At the same time, we keep an open mind regarding combinations of technological solutions in the overall energy system. This ensures that the technology pathways with the greatest climate neutrality potential are easily identified. These focal points can subsequently serve in, e.g., narrative-driven scenario analyses to define comprehensive guides for action for policymakers. A case study of Austria for the proposed potential analysis demonstrates that bio-CH4 and electrolysis-derived H2 are the most economical green gases, but GHG certificate costs will be necessary for cost-competitiveness in high-temperature applications. Electrification offers advantages over conventional technologies and CO2-neutral gas alternatives in low-to-mid temperature ranges. Under the given assumptions, including GHG emission certificate costs of 250 EUR/t CO2, alternative technologies in the identified climate neutrality pathways can operate at total annual costs comparable to conventional fossil-based equivalents. [ABSTRACT FROM AUTHOR]

In the present work, the operating results from an innovative, renewable, energy-based space-heating and domestic hot water (DHW) system are shown. The system used solar thermal energy as its primary source and was assisted by a shallow geothermal application in order to accommodate the space-heating and DHW needs of a domestic building in Austria. The system incorporated phase-change materials (PCMs) in specially designed containers to function as heat-storage modules and provide an energy storage capability for both the space-heating and DHW subsystems. This system was designed, implemented, and tested under real operating conditions in a building for a period of one year. The operating and energy results for the system are demonstrated in this work. The system was compared with a conventional one, and a reduction in the primary energy consumption equal to 84.3% was achieved. The maintenance and operating costs of the system were reduced by 79.7% compared to the conventional system, thus significantly contributing to the NZEB target of the building. The newly proposed system, although presenting an increased operating complexity, utilizes an innovative self-learning control system that manages all of its operations. The combination of a solar thermal energy source with thermal energy storage increases the use of renewable energy by extending the capacity of the system beyond the solar hours and using excess solar energy for space-heating needs. The thermal energy storage unit also increases the energy and economic efficiency of the geothermal heat pump by operating it during the hours of a reduced electricity tariff and using the stored energy during hours of a high electricity demand. The cost for the installation of such a system is higher than a conventional one, but due to the significantly decreased operating costs, the pay-back period was calculated to be 8.7 years. [ABSTRACT FROM AUTHOR]

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid. [ABSTRACT FROM AUTHOR]