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This paper delves into the pivotal role of water electrolysis (WE) in green hydrogen production, a process utilizing renewable energy sources through electrolysis. The term "green hydrogen" signifies its distinction from conventional "grey" or "brown" hydrogen produced from fossil fuels, emphasizing the importance of decarbonization in the hydrogen value chain. WE becomes a linchpin, balancing surplus green energy, stabilizing the grid, and addressing challenges in hard-to-abate sectors like long-haul transport and heavy industries. This paper navigates through electrolysis variants, technological challenges, and the crucial association between electrolytic hydrogen production and renewable energy sources (RESs). Energy consumption aspects are scrutinized, highlighting the need for optimization strategies to enhance efficiency. This paper systematically addresses electrolysis fundamentals, technologies, scaling issues, and the nexus with energy sources. It emphasizes the transformative potential of electrolytic hydrogen in the broader energy landscape, underscoring its role in shaping a sustainable future. Through a systematic analysis, this study bridges the gap between detailed technological insights and the larger energy system context, offering a holistic perspective. This paper concludes by summarizing key findings, showcasing the prospects, challenges, and opportunities associated with hydrogen production via water electrolysis for the energy transition. [ABSTRACT FROM AUTHOR]

Heritage from the COVID-19 period (in terms of massive utilization of mechanical ventilation systems), global warming, and increasing electricity prices are new challenging factors in building energy management, and are hindering the desired path towards improved energy efficiency and reduced building consumption. The solution to improve the smartness of today's building and automation control systems is to equip them with increased intelligence to take prompt and appropriate actions to avoid unnecessary energy consumption, while maintaining a desired level of air quality. In this manuscript, we evaluate the ability of machine-learning-based algorithms to predict CO2 levels, which are classic indicators used to evaluate air quality. We show that these algorithms provide accurate forecasts (more accurate in particular than those provided by physics-based models). These forecasts could be conveniently embedded in control systems. Our findings are validated using real data measured in university classrooms during teaching activities. [ABSTRACT FROM AUTHOR]

In the perspective of sustainable cities, urbanist and planners have to deal with a constantly increasing penetration of renewable energy systems (RES) in the urban structure: in tertiary sector, retail and supermarket stores as particularly energy-intensive compounds play a core role in this scenario, as they are often characterized by similar kinds of structures mostly, large surfaces and a single floor, and analogous composition of energy loads. Most of the current research focuses on energy efficiency, but these buildings are particularly interesting for a systematic application of PhotoVoltaic (PV) generation technology as the energy uses associated with them are mainly electrical, in connection with air conditioning and food cooling. This article analyses the energy consumption in the supermarket sector for the sale of food. After an analysis of the main energy consumption parameters, standard solutions for the sizing of PV systems are proposed based on different design objectives, highlighting the potential of each proposed solution. Results show that a high percentage of self-consumption can be achieved, and that a battery storage set at a mean daily PV potential production level (4 kWh/kW in the case) perfectly suits to reach a self-sufficiency between 50-70%. Retail and food stores have proven to be a perfect promoter for PV diffusion either in a high self-consumption configuration, or turning them into energy hub for mobility to building or energy sharing policies.

Data from the International Energy Agency confirm that in a zero-energy perspective the integration of solar systems in buildings is essential. The development of passive solar strategies has suffered the lack of standard performance indicators and design guidelines. The aim of this paper is to provide a critical analysis of the main passive solar design strategies based on their classification, performance evaluation and selection methods, with a focus on integrability. Climate and latitude affect the amount of incident solar radiation and the heat losses, while integrability mainly depends on the building structure. For existing buildings, shading and direct systems represent the easiest and most effective passive strategies, while building orientation and shape are limited to new constructions: proper design can reduce building energy demand around 40%. Commercial buildings prefer direct use systems while massive ones with integrated heat storage are more suitable for family houses. A proper selection must consider the energy and economic balance of different building services involved: a multi-objective evaluation method represents the most valid tool to determine the overall performance of passive solar strategies.

The COVID-19 pandemic is changing the way individuals, worldwide, feel about staying in public indoor spaces. A strict control of indoor air quality and of people’s presence in buildings will be the new normal, to ensure a healthy and safe environment. Higher ventilation rates with fresh air are expected to be a requirement, especially in educational buildings, due to their high crowding index and social importance. Yet, in this framework, an increased use of primary energy may be overlooked. This paper offers a methodology to efficiently manage complex HVAC systems in educational buildings, concurrently considering the fundamental goals of occupants’ health and energy sustainability. The proposed fourstep procedure includes: dynamic simulation of the building, to generate synthetic energy loads; clustering of the energy data, to identify and predict typical building use profiles; day-ahead planning of energy dispatch, to optimize energy efficiency; dynamic adjustment of air changes, to guarantee a safe indoor air quality. Clustering and forecasting energy needs are expected to become particularly effective in a highly regulated context. The technique has been tested on two university classroom buildings, considering pre-lockdown attendance. This notwithstanding, quality and significance of the obtained thermal energy clusters push towards a benchmark post-pandemic application.