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The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution.

This open access book brings together a collection of cutting-edge insights into how action can and is already being taken against climate change at multiple levels of our societies, amidst growing calls for transformative and inclusive climate action. In an era of increasing recognition regarding climate and ecological breakdown, this book offers hope, inspiration and analyses for multi-level climate action, spanning varied communities, places, spaces, agents and disciplines, demonstrating how the energy and dynamism of local scales are a powerful resource in turning the tide. Interconnected yet conceptually distinct, the book’s three sections span multiple levels of analysis, interrogating diverse perspectives and practices inherent to the vivid tapestry of climate action emerging locally, nationally and internationally. Delivered in collaboration with the UK’s ‘Place-Based Climate Action Network’, chapters are drawn from a wide range of authors with varying backgrounds spread across academia, policy and practice.

In recent years, the accelerating climate change and intensifying natural disasters have called for more renewable, resilient, and reliable energy from more distributed sources to more diversified consumers, resulting in a pressing need for advanced situational awareness of modern smart distribution systems [...]

The phenomena of climate change transcend all national and regional boundaries. To address this complex challenge, we must determine the areas of the country of interest, in this case, Greece, that have been most adversely affected by climate. Greece is surrounded by water, and a significant part of its GDP is derived from the marine and maritime industries, including tourism. Since the start of the IntelComp project, a Preparatory Living Lab (PLL) has been planned and delivered, feeding into the development of the IntelComp platform and the Living Lab on Climate Change Adaptation. The study's results lead to the conclusion that one of the most important challenges in tackling climate change is the decarbonisation challenge, specifically the shift to renewable energy sources and the investments that must be made. Several EU and national policy frameworks, including the European Green Deal, the Climate Law, the National Long-term Strategy for 2050 (on the Climate and Energy), highlight the decarbonisation as one of the major challenges in the climate change pledge. This will be the primary subject of the IntelComp climate change case study. PLLs also led to the identification of policy questions and useful data sources to aid the IntelComp project's launch. While previous research on co-production has primarily focused on involving citizens through public participation processes in order to gain their support, trust, and insights in structured decision-making processes, our approach opens a new channel for incorporating external knowledge into problem-solving processes. The IntelComp project will aid in policy development by providing pertinent tools co-developed with the final users that will provide insights and analysis in the field of STI (Science, Technology, Innovation) encompassing all of the Energy areas mentioned above.

This study aims to introduce, conceptualize, and design a novel biomass/gasification-driven hybrid energy configuration. The proposed hybrid configuration has four subsystems: reformer solid oxide fuel cell (RSOFC), biomass/gasification, homogeneous charge compression ignition engine (HCCIE) plus waste heat recovery system (WHRS). RSOFC and HCCIE systems are embedded to generate electric energy. The syngas required for these two subsystems is captured from the biomass/gasification subsystem. In addition to generating electrical energy, fuel cell is responsible for providing combustible fuel to the HCCIE subsystem. The embedded engine in the system can improve the proposed configuration efficiency by increasing the rate of electrical energy production. In addition, the dissipated heat of fuel cell and engine subsystems is recovered by WHRS. The proposed energy configuration is evaluated and discussed from energetically, exergetically and exergoeconomic and environmental aspects to obtain a comprehensive feasibility study of the plant. The offered hybrid design has new component's structure and relationships that have not been reported in the publications. The analysis indicated that the proposed hybrid configuration is capable of generating approximately 1100 kW and 366.3 W of electric and thermal power, respectively, with the overall energetic and exergetic efficiencies of 69.4% and 52.1%. Exergoeconomic analysis results revealed that the specific fuel cost of the total proposed configuration was approximately 1.96 USD per GJ. In addition, compared to a coal and petroleum oil-based power generation plants, the proposed hybrid configuration can have approximately 2.75-fold and 97.7% lower CO2 emissions, sequentially. Besides, the proposed system can rival other similar biomass-driven designs.

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.

Researchers have been driven to investigate sustainable alternatives to cement production, such as geopolymers, due to the impact of global warming and climate change resulting from greenhouse gas emissions. Currently, they are exploring different methods and waste materials to enhance the mechanical and physical properties of geopolymer and expand its application range. This review paper offers a thorough analysis of the utilization of various waste materials in geopolymer manufacturing and shows the creative contribution of this research to the development of environmentally friendly cement substitutes. The article covers the properties, durability, and practical applications of geopolymer composites made from various waste binders. It includes a microstructure and chemical analysis. The research findings indicate that geopolymers are an effective cementitious binder substitute for cement in various applications. Additionally, the ecological and carbon footprint analysis highlights the sustainability of geopolymers compared to cement.