Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images

In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.Peer reviewe

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Urban vertical farming with a large wind power share and optimised electricity costs

Producing food in an environmentally sustainable way for the growing human population is a challenge to the global food system. Vertical farm (VF) as a part of the solution portfolio is attracting interest since it uses less water, pesticides, and land which are scarce in many parts of the globe. Despite these positive factors, the energy demand for vertical farms is high, and farms often remain separate and excluded from cities where most of the population lives. City-level energy system solutions exist to empower energy efficiency and increase the share of variable renewable energy sources, but their potential has not yet been estimated for an urban energy system that includes large vertical farms. Accordingly, in this study, we simulate an urban energy system that practices vertical farming with large-scale variable renewable energies and flexibility measures. For the first part of the study, we modelled a vertical farm's energy system with demand response control to maximize electricity cost savings. To evaluate the potential of demand response, the analysis is carried out for different crops (lettuce, wheat, and soybean), and different electricity price profiles. The result of demand response control can be a reduction of 5% to 30% in electricity consumption costs. Further, sensitivity analyses highlight the effect of electricity price variations and photoperiod on demand response outcomes. In the second part, the operation of an urban energy system (Helsinki, Finland) with vertical farms was analysed through two different scenarios. These scenarios represent the emission-free Helsinki energy system in 2050 with large-scale wind power implementation. As VFs can use electricity outside the peak demand hours, the inclusion of VF with the right energy system configuration can improve the power consumption within the system by up to 19%. Further, we show that connection to the exogenous power market is important to support vertical farming in the future energy systems. In this study, key points in the integration of VF in urban energy systems are highlighted, including the role of exogenous power markets, the potential for increasing local energy consumption with large wind power, and the importance of crop selection in reducing VF's energy costs through demand response. In a city-level solution with a high wind power share, we thus recommend including a vertical farm side by strong sectoral coupling as part of the future design to maximise local consumption

Current and Novel Emerging Medical Therapies for Peripheral Artery Disease: A Literature Review

Despite the improvements in endovascular techniques during the last decades, there is still an increase in the prevalence of peripheral artery disease (PAD) with limited practical treatment, which timeline impact of any intervention for critical limb ischemia (CLI) is poor. Most common treatments are not suitable for many patients due to their underlying diseases, including aging and diabetes. On the one hand, there are limitations for current therapies due to the contraindications of some individuals, and on the other hand, there are many side effects caused by common medications, for instance, anticoagulants. Therefore, novel treatment strategies like regenerative medicine, cell-based therapies, Nano-therapy, gene therapy, and targeted therapy, besides other traditional drugs combination therapy for PAD, are newly considered promising therapy. Genetic material encoding for specific proteins concludes with a potential future for developed treatments. Novel approaches for therapeutic angiogenesis directly used the angiogenetic factors originating from key biomolecules such as genes, proteins, or cell-based therapy to induce blood vessel formation in adult tissues to initiate the recovery process in the ischemic limb. As PAD is associated with high mortality and morbidity of patients causing disability, considering the limited treatment choices for these patients, developing new treatment strategies to prevent PAD progression and extending life expectancy, and preventing threatening complications is urgently needed. This review aims to introduce the current and the novel strategies for PAD treatment that lead to new challenges for relief the patient’s suffered from the disorder

Modeling and optimization of urban energy systems for large-scale integration of variable renewable energy generation

Defense is held on 10.9.2021 12:00 – 16:00 (Zoom), https://aalto.zoom.us/j/69170835429To meet the future emissions goals, the energy systems need to be decarbonized. As much of the energy use originates from urban areas, their role will be of key importance in this context. One strategy for decarbonization is to use large-scale variable renewable electricity schemes, but these include several challenges, notably the issue of supply and demand mismatch. Therefore, a mix of technologies may be needed to achieve ambitious decarbonization targets in cities. The aim of this doctoral thesis is to develop solutions for city-level energy system transition. For this purpose, a dynamic energy system model for Helsinki city is used to ana-lyze a range of scenarios for a low-carbon future. Renewable energy, in particular wind power, was chosen here as the key supply technology. As northern cities are heat-dominated, the heat-ing sector was included in the analysis by using power-to-heat and heat pump schemes in par-allel to power production. To meet short peak heat demand conditions, separate bio-boilers were also considered. Such schemes provided deep decarbonization possibilities. In the Hel-sinki case, the use of fossil fuels could be reduced even up to 70% through the coupling of wind power with curtailment and heat pumps. Though the above type of sectoral coupling to heating helps to integrate large amounts of intermittent renewable power, the role of the exog-enous power market proved to be important for wind power integration. For Helsinki, for ex-ample, with a wind power capacity of 1500 MW corresponding to 62% of the annual electricity demand, 89% of the wind electricity could be used locally in the different sectors, but the rest needs to be coupled to the exogenous market due to the mismatch and plant limitations. In-corporating demand-side measures, e.g., building energy efficiency, could save 6%-13% in the annual system costs. Other alternatives such as sustainable gas were also investigated. The results of this thesis indicate that there are several decarbonization pathways of the urban energy systems of which some could even yield a zero-emission energy system

Building data model identification and predictive demand response control

Data-based models are conceptual tools for describing the real world entities and the connection among them. There are a lot of studies with usage of data-based structures for forecasting, optimization and fault detection in building energy systems. The main objectives of this thesis have been devoted to evaluate how data-based modelling can contribute to energy building system identification and demand response control. In overall view, this research work contains the separate sections for identifying energy system of buildings and establishing the optimal demand response control. In first stage of this research, multi stages prediction algorithm has been developed by means of a novel combination of signal processing technique (wavelet transformation) and dynamic neural network. Different prediction algorithms have been examined for forecasting the energy demand of building with considering random profiles in occupancy, lighting and equipment profiles. Also ability of model in forecasting energy demand has been investigated for different type of building structures and insulation levels. The model predictive control then has been organized for achieving the optimal energy trading schedule in energy building systems. The system includes building, Photovoltaic (PV) component, heat storage tank and ground source heat pump (GSHP). The heat demand of building contains space heating and domestic hot water demands. Active thermal storage is used for saving energy regarding to dynamic electricity price. Then based on dynamic coefficient of performance (COP) for GSHP, the required electricity of compressor has been achieved. On-site energy generated by PV system has been balanced with electricity demand of system to support trading with grid. The applied predictor structure improves the time of applying control algorithms for building energy systems signifi-cantly. The applied control methodology and on-site energy generation also save the energy cost as 28% and energy consumption as 17% for passive massive buildings. The results highlight the adaptability of proposed algorithm in prediction and identifying the energy system for different building structures and insulation levels. The comparative results also reveal the priority of the proposed method in aspect of prediction accuracy as compared to neural network. The integration of signal analysis and dynamic neural network is strong alternative for common simulation software in applying and evaluation of different control systems

Construction cost estimation of spherical storage tanks

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg–Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.Peer reviewe

Effect of heat demand on integration of urban large-scale renewable schemes-case of Helsinki City (60 °n)

Heat demand dominates the final energy use in northern cities. This study examines how changes in heat demand may affect solutions for zero-emission energy systems, energy system flexibility with variable renewable electricity production, and the use of existing energy systems for deep decarbonization. Helsinki city (60 °N) in the year 2050 is used as a case for the analysis. The future district heating demand is estimated considering activity-driven factors such as population increase, raising the ambient temperature, and building energy efficiency improvements. The effect of the heat demand on energy system transition is investigated through two scenarios. The BIO-GAS scenario employs emission-free gas technologies, bio-boilers and heat pumps. The WIND scenario is based on large-scale wind power with power-to-heat conversion, heat pumps, and bio-boilers. The BIO-GAS scenario combined with a low heat demand profile (-12% from 2018 level) yields 16% lower yearly costs compared to a business-as-usual higher heat demand. In the WIND-scenario, improving the lower heat demand in 2050 could save the annual system 6-13% in terms of cost, depending on the scale of wind power.Peer reviewe

Coupling Variable Renewable Electricity Production to the Heating Sector through Curtailment and Power-to-heat Strategies for Accelerated Emission Reduction

The Paris Climate Accord and recent IPCC analysis urges to strive towards carbon neutrality by the middle of this century. As most of the end-use energy in Europe is for heating, or well above 60%, these targets will stress more actions in the heating sector. So far, much of the focus in the emission reduction has been on the electricity sector. For instance, the European Union has set as goal to have a carbon-free power system by 2050. Therefore, the efficient coupling of renewable energy integration to heat and heating will be part of an optimal clean energy transition. This paper applies optimization-based energy system models on national (Finland) and sub-national level (Helsinki) to include the heating sector in an energy transition. The models are based on transient simulation of the energy system, coupling variable renewable energies (VRE) through curtailment and power-to-heat schemes to the heat production system. We used large-scale wind power schemes as VRE in both cases. The results indicate that due to different energy system limitations and boundary conditions, stronger curtailment strategies accompanied with large heat pump schemes would be necessary to bring a major impact in the heating sector through wind power. On a national level, wind-derived heat could meet up to 40% of the annual heat demand. On a city level, the use of fossil fuel in combined heat and power production (CHP), typical for northern climates, could significantly be reduced leading even close to 70% CO2 emission reductions in Helsinki. Though these results were site specific, they indicate major opportunities for VRE in sectoral coupling to heat production and hence also a potential role in reducing the emissions.Peer reviewe

A cost-optimal solar thermal system for apartment buildings with district heating in a cold climate

Finding the global optimal combination of the main components for a solar thermal energy system is an important topic in utilising solar radiation in a cost-effective way. However, selecting an optimal solar thermal system in a cold climate condition is a challenging task due to the dependency on the heat demand and the limited availability of solar radiation. This research presents several sets of optimum combinations of a solar thermal collector and a hot water storage tank regarding energy efficiency and the life cycle cost. Since domestic hot water consumption forms the significant part of the heat demand in new energy efficient apartment buildings, the applied consumption information were extracted precisely according to measured data. The solar thermal system with cost-optimal component sizes was able to save district heat energy consumption up 24% to 34% and made 4 €/m^2 to 23 €/m^2 in financial profit.Peer reviewe