Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit nature of water flow paths makes it challenging to extract a priori knowledge of the connectivity structure. We hypothesize that terrain elevation significantly affects flow and connectivity. To incorporate this, we use LiDAR terrain elevation data encoded through a Vision Transformer (ViT). The ViT, which has demonstrated excellent performance in image classification by directly applying transformers to sequences of image patches, efficiently captures spatial features of terrain elevation. To account for both spatial and temporal features, we employ GRU blocks enhanced with graph convolution, a method widely used in the literature. We propose a hybrid graph learning structure that combines static and dynamic graph learning. A static graph, derived from transformer-encoded LiDAR data, captures terrain elevation relationships, while a dynamic graph adapts to temporal changes, improving the overall graph representation. We apply graph convolution in two layers through these static and dynamic graphs. Our method makes daily predictions up to 12 days ahead. Empirical results from multiple water stations in Quebec demonstrate that our method significantly reduces prediction error by an average of 10% across all days, with greater improvements for longer forecasting horizons.

In light of escalating extreme events and climate change, this research focuses on understanding energy consumption in buildings, specifically under varied weather scenarios including past (2019-2022) and future projections (2061, 2099). Traditional building simulations stemming from representative, using typical year’s weather data doesn't capture the intricacies of long-term climate shifts especially for the future. To address this, this study incorporates detailed future climate data from combination of RCMs & GCMs. This data is used in combination with open-geospatial data to create a building geometry. Initial results highlight a shift to warmer temperatures in 2061 and 2099. When contrasted with a typical mean weather scenario (1960-1986), there's a noticeable increase in cooling energy and a decrease in heating energy consumption from 2019-2022. By 2099, overall energy use is predicted to decrease by 10%-30%, which when broken down constitutes to reduction in heating energy and increase in cooling energy. The research underscores the impending shift towards increased cooling demands and reduced heating needs. The findings emphasize the urgency for future building designs to be energy-efficient and resilient in the face of evolving climate conditions.

This paper presents a concept and first glimpse at the development of an urban digital twin framework to estimate and forecast the carbon footprints of urban neighbourhoods, with a focus on household consumption choices, specifically in buildings, food, and transportation sectors, as key emission contributors. Despite constituting nearly three-quarters of global carbon emissions, the influence of household consumption choices on a region’s carbon footprint is often neglected. While assessments at a regional or city scale may prove too broad for targeted mitigation strategies, estimating carbon emissions at the neighbourhood scale can foster sustainable and resilient urban areas. However, challenges arise in estimating emissions at this scale due to the availability of aggregated data, insufficient cross-sectoral data integration, and a lack of practical visualisation tools, causing policymakers to overlook the impact of household choices on neighbourhood carbon footprints. Therefore, the present article provides insights into the ongoing early-stage development of using urban digital twins to model, simulate, analyse, and visualise the impact of household consumption choices on neighbourhood-scale consumption-based carbon emissions. By exploring ”what-if” scenarios, this research also seeks to forecast emission profiles based on how household consumption choices influence a neighbourhood’s carbon emissions under future climatic and demographic conditions.

This study compares two renowned methodologies, API 650 and API 579, focusing on the analysis of joint efficiency at a value of 0.7. Using Finite Element Analysis (FEA), the research suggests that a 35 % increase in filling height might be achievable for a large tank that adhere to the stability criteria outlined by API 650. To support these findings, 337 simulations rigorously examined various parameters. These encompass the design factor (beta), bottom constraint, geometric configuration, mesh size, and a newly introduced Local ASME criterion. The latter is specifically introduced to evaluate protection against plastic collapse for Maximum Fill Height (MFH). Additionally, the study advocates elevating the joint efficiency from 0.7 to a range of 0.8-0.87 in API 653. This recommendation is pertinent to storage tanks that are not susceptible to buckling failure mode and possess limited documentation. The outcomes of this research provide significant insights into tank design and have the potential to refine industry standards and practices.

The interconnectivity between building-scale and urban-scale modeling is beneficial for building energy assessment. The energy consumption estimation of individual buildings could be affected by numerous factors from the surrounding environment. The purpose of this study is to compare the energy consumption of a stand-alone building versus the same building modeled within the urban context. A case study high-rise building in Montreal was investigated in detail in its urban context to analyze the impact of shading on building energy demand. Three scenarios are introduced for the surrounding buildings: high-rise, low-rise, and the actual context. Each scenario's effect on the target building's energy consumption is estimated and compared with the stand-alone condition. The impact of each alternative on building performance was assessed by calculating yearly total energy consumption (heating, cooling). The results show that shading due to the nearby buildings plays an essential role in energy demand throughout the year. Increasing the height of the surrounding buildings in winter increases the heating consumption by up to 44\%, and a reduction in cooling by up to 40\% is seen during summertime. This study confirms that considering the effect of neighbor buildings does affect the energy-related simulation's outcomes. Therefore, building energy behavior analysis in urban and street planning can be the theoretical foundation for logical architecture design and energy consumption reduction when efficient cities are constructed. Moreover, the influence of other urban environmental factors, such as meteorological loads, Urban Heat Island (UHI) effects, or urban morphology, could be investigated for future studies.

The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly interconnected water systems can significantly affect hydrometric forecasting. Consequently, it is crucial to develop models that represent the relationships between other system components. In recent years, numerous hydrological applications have been studied, including streamflow prediction, flood forecasting, and water quality prediction. Existing methods are unable to model the influence of adjacent regions between pairs of variables. In this paper, we propose a spatiotemporal forecasting model that augments the hidden state in Graph Convolution Recurrent Neural Network (GCRN) encoder-decoder using an efficient version of the attention mechanism. The attention layer allows the decoder to access different parts of the input sequence selectively. Since water systems are interconnected and the connectivity information between the stations is implicit, the proposed model leverages a graph learning module to extract a sparse graph adjacency matrix adaptively based on the data. Spatiotemporal forecasting relies on historical data. In some regions, however, historical data may be limited or incomplete, making it difficult to accurately predict future water conditions. Further, we present a new benchmark dataset of water flow from a network of Canadian stations on rivers, streams, and lakes. Experimental results demonstrate that our proposed model TransGlow significantly outperforms baseline methods by a wide margin.

With the switching from fossil to renewable fuels, a wide range of cogeneration technologies needs to be reassessed to analyze their efficiency, lifetime, and maintenance performance for new fuels. Depending on the application, efficiency priorities, and power range, different cogeneration technologies have been compared in this study. Fuel cells provide the highest efficiency and lowest emissions, but costs remain high, and their lifetime is lower than gas turbines. They can be used in aviation because of the lowest fuel consumption in cruise and stationary states; however, they are limited to short-range flights because refueling aircraft poses serious hydrogen leakage risks, and insulated storage tanks of hydrogen are excessive in volume and weight. Like fuel cells, micro-gas turbines and Stirling engines are used for lower power, while they have lower electrical efficiency (30% and 15% vs. 50% fuel cell efficiency for 100 kW). For large-scale applications, gas turbines and internal combustion engines are used, with lower cost, and faster ramp-up times as an advantage of ICE, while emissions are lower for GT. Using hydrogen in such systems increases efficiencies due to the higher heating value, while syngas performs less well but offers low-cost solutions, and existing infrastructure can be used. Additional cost occurs due to other hydrogen infrastructure, storage, leakage, and flame flashback risk prevention. In conclusion, cogeneration technologies offer ample scope for low emission, reliable and flexible power in future renewable energy systems and need to be carefully chosen depending on the application, power range, and performance goals.

The integration of heat pumps into DHC (District Heating and Cooling) networks provides significant environmental and performance improvements, an innovative and profitable solution for different decarbonizing sectors. This work reviews different district heating and cooling networks and the integration of high-temperature commercial heat pumps. Likewise, it describes placement options and connection modes of a heat pump unit in DHC networks, identifying twelve generic configurations of heat pumps and how they can be integrated into DHC systems, where four (4) of them have not been studied in the literature. The 3G, and 4G, are the most common district heating and cooling network generations for reference conditions reviewed. When considering a case where a central heat pump and CHP plant were located in the DHC network, the resulting coefficient of system performance (COSP) was in the range of 3–4 for commercial equipment with a range of coefficients of performance (COP) between 2 and 6. Using local HPs in the fourth and fifth-generation district heating networks, the resulting COSP was in the range of 0.95–1.5 for commercial equipment with a range of coefficients of performance (COP) between 2 and 6. Finally, for individual HPs in the district heating and cooling network, the resulting COSP was in the range 3–4 for commercial equipment with a range of coefficients of performance (COP) between 2 and 8.

Time-resolved, occupancy-dependent electricity load profiles at building level for city quarters or entire cities are important for planning authorities, project developers, utilities or other stakeholders in order to develop energy saving strategies and meet climate targets. Firstly, this information enables a more accurate modelling of renewable energy systems. Secondly, aspects like sector coupling, storage decisions and the impact of technologies such as electric vehicles or heat pumps on the grid can be considered. Thirdly, it allows a more detailed economic analysis. This paper contains the newly added features to the simulation environment SimStadt, which is used for strategic modelling of sustainable urban or regional areas with a spatial resolution at the building level. SimStadt interlinks 3D CityGML models with parameters for buildings physics to simulate energy demands and renewable energy potential. It was enhanced by the development of an electricity load profile generator with variable resolution and the addition of an hourly resolved PV potential analysis including a variable economic analysis. This enables e.g. the evaluation of photovoltaic potential with the associated investment, operating and levelized costs over the lifetime of hundreds of individual buildings in parallel. Together with additional electric building demand from heat pumps, electric vehicles or load shifting options through the use of battery storage, it will be possible to assess and compare the feasibility, benefits and economic viability of energy/electricity-related urban renewal measures in even greater detail and with a holistic perspective. The simulation platform enables the development of granular sustainable urban (sub)strategies and energy concepts through a holistic, time-resolved, building-specific approach to support transformation of the building stock to a sustainable, low-carbon one.

The present study investigates the steam gasification of palm kernel shells (PKS) for hydrogen-rich N-2-free syngas production in a pilot-scale atmospheric downdraft allothermal gasifier. The feedstock material characterization, description of the gasifier design, auxiliary systems, operating conditions, and evaluation of the operating methodology for obtaining fuel gas are presented. The gasification equipment was tested under gasification temperatures between 800 and 950 degrees C and steam-biomass mass ratios between 0.2 and 1.2. The maximum H-2 generation is achieved with a gasification temperature of 850 degrees C, steam/biomass ratio of 0.85, particle sizes of 2-3 mm, and biomass feed of 57.1 kg/h. The volume fraction of H2+ CO can reach 80.4%, and maximum cold gas efficiency of 80% with an average lower calorific value of 11.5 MJ/Nm(3)

The main contribution of the present work consists of the design and thermal characterization of an alkaline electrolysis cell for hydrogen generation at atmospheric pressure. The electrolytic cell was manufactured from acrylic, using 316 L stainless steel electrodes and considering a membrane separation for gases. The effect of current conditions, the distance between electrodes on the production efficiency of hydrogen and the distribution and variation of temperatures on the surface of the electrodes in operation were evaluated. Maximum hydrogen generation was achieved with a separation between electrodes (anode and cathode) of 3 mm and a current of 30 A at 12 V. Furthermore, the thermoelectric effects on the electrodes were analyzed, and the presence of areas of higher activity was discussed for oxidation and reduction reactions.

District heating and cooling (DHC) networks play a fundamental role in the transition towards a sustainable supply of heating and cooling, due to their ability to integrate any available source of thermal energy and to distribute it to the buildings. However, the use of renewable non-constant sources together with the variable heat demand of the buildings creates instable and pronounced transient operating conditions. In order to analyse the hydraulic and thermal behaviour and the dynamics occurring within these networks, several physical models based on different methods were proposed by previous researchers. Numerical thermal models based on finite difference methods (FDM) were pointed out to suffer from artificial diffusion when simulating the propagation of heat through the network. However, due to a wide and well-known literature on these methods, they are still used by many researchers and are therefore worth being investigated. The present paper analyses the effects of artificial diffusion using two models based on two different first-order approximation schemes. An ideal temperature wave and a dataset from a real DH network were used to evaluate the accuracy of the models using different discretization levels in time and space. As a result, the paper provides a framework to set a proper discretization when simulating a DHC network with FDM-based models considering both the expected accuracy and the computation time as criteria.

In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding the uncertainty of the estimated parameters of the models. Traditionally, the dynamic models for these complex components are derived from the heat transfer balance equations, and the unknown heat transfer coefficients (convection and radiation), the solar properties of the materials or the pressure coefficients for the air mass flow rate balance, are assigned based on literature or on manufacturer prescriptions. However, there is a lack of systematic methods able to validate the simulation outputs with the measured data, taking into consideration the uncertainty of the parameters and their effect over the results. This research is focused on the development of a dynamic simulation model for a PV ventilated component, and on the application of a data-driven iterative approach to identify the unknown parameters, to evaluate their influence in the simulation outputs and finally, to determine the deviations of the simulations outputs against the measured data. During the identification process, 43 unknown parameters are detected and 13 of them are categorized as strong parameters. The implemented data driven approach is able to achieve high goodness of fit with the measured data and it is recommended to analyses which aim at evaluating the influence of some component parameters or the thermal and electrical energy produced by these natural ventilated PV components. (C) 2016 Published by Elsevier Ltd.

Analogous to the use of solar energy for heating and cooling, geothermal technologies provide the same opportunities for using renewable sources in buildings. This chapter examines how the energy input side of these technologies differs from solar applications. A detailed examination of efficiency and economic effects informs the decisions required for planning with these technologies.