| 11011. |
- Mohammadi, Babak, et al.
(författare)
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The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery
- 2023
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Ingår i: GIScience and Remote Sensing. - 1548-1603. ; 60:1
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Tidskriftsartikel (refereegranskat)abstract
- Accurate monitoring of glaciers’ extents and their dynamics is essential for improving our understanding of the impacts of climate and environmental changes in cold regions. The satellite-based Normalized Difference Snow Index (NDSI) has been widely used for mapping snow cover and glaciers around the globe. However, mapping glaciers in snow-covered areas using existing indices remains a challenging task due to their incapabilities in separating snow, glaciers, and water. This study aimed to evaluate a new satellite-based index and apply machine learning algorithms to improve the accuracy of mapping glaciers. A new index based on satellite data from Sentinel-2 was tested, which we call the Adjusted Normalized Difference Snow Index (ANDSI). ANDSI (besides NDSI) was used with five different machine learning algorithms, namely Artificial Neural Network, C5.0 Decision Tree Algorithm, Naive Bayes classifier, Support Vector Machine, and Extreme Gradient Boosting, to map glaciers, and their performance was evaluated against ground reference data. Four glacierized regions in different countries (Canada, China, Sweden, and Switzerland-Italy) were selected as study sites to evaluate the performance of the proposed ANDSI. Results showed that the proposed ANDSI outperformed the original NDSI, and the C5.0 classifier showed the best overall accuracy and Kappa among the selected five machine learning classifiers in the majority of cases. The original NDSI yielded results with an average overall accuracy of (around) 91% and the proposed ANDSI with (around) 95% for glacier mapping across all models and study regions. This study demonstrates that the proposed ANDSI serves as a superior and improved method for accurately mapping glaciers in cold regions.
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| 11012. |
- Mohammadi, Saeed, 1989-, et al.
(författare)
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A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets
- 2022
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Ingår i: 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Prosumer operators are dealing with extensive challenges to participate in short-term electricity markets while taking uncertainties into account. Challenges such as variation in demand, solar energy, wind power, and electricity prices as well as faster response time in intraday electricity markets. Machine learning approaches could resolve these challenges due to their ability to continuous learning of complex relations and providing a real-time response. Such approaches are applicable with presence of the high performance computing and big data. To tackle these challenges, a Markov decision process is proposed and solved with a reinforcement learning algorithm with proper observations and actions employing tabular Q-learning. Trained agent converges to a policy which is similar to the global optimal solution. It increases the prosumer's profit by 13.39% compared to the well-known stochastic optimization approach.
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| 11013. |
- Mohammadi, Saeed, 1989-, et al.
(författare)
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Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data
- 2022
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Ingår i: 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- The intraday (ID) electricity market has received an increasing attention in the recent EU electricity-market discussions. This is partly because the uncertainty in the underlying power system is growing and the ID market provides an adjustment platform to deal with such uncertainties. Hence, market participants need a proper ID market price model to optimally adjust their positions by trading in the market. Inadequate historical data for ID market price makes it more challenging to model. This paper proposes long short-term memory, deep convolutional generative adversarial networks, and No-U-Turn sampler algorithms to model ID market prices. Our proposed econometric ID market price models are applied to the Nordic ID price data and their promising performance are illustrated.
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| 11014. |
- Mohiti Ardakani, Maryam, 1989, et al.
(författare)
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A Decentralized Transactive-Based Model for Reactive Power Ancillary Service Provision by Local Energy Communities
- 2023
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Ingår i: 2023 IEEE Belgrade PowerTech, PowerTech 2023.
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Konferensbidrag (refereegranskat)abstract
- Local Energy Communities (LECs) have been introduced to facilitate the high penetration of distributed energy resources into distribution systems (DSs), which can also be valuable sources of ancillary services for Distribution System Operators (DSOs). The aim of this paper is to investigate if and how LECs can contribute to the reactive power management of DSs. To this end, a transactive-based model is proposed to manage the reactive power of LECs in a decentralized way. In the proposed model, DSO sends transactive signals to LECs and incentivizes them to inject/absorb reactive power aiming at controlling the voltage profile of DS. Accordingly, the reactive power management and thus voltage control is performed without any direct interferences of the DSO in the resource scheduling of LECs. The proposed model is applied to the DS of Chalmers campus to demonstrate its performance in reactive power management and voltage control.
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| 11015. |
- Mohiti Ardakani, Maryam, 1989, et al.
(författare)
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A Risk-Averse Energy Management System for Optimal Heat and Power Scheduling in Local Energy Communities
- 2022
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Ingår i: 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022.
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Konferensbidrag (refereegranskat)abstract
- Local energy communities (LECs) facilitate energy distribution, supply, consumption, storage, and trading for the communities and their members. This paper proposes a risk-averse energy management system (EMS) for optimal heat and power scheduling in LECs. Three approaches namely high accuracy forecast models, advanced optimization models, and providing flexibility sources are followed to handle uncertainties of photovoltaic power and load. To this end, the load demand and photovoltaic power as uncertain variables are predicted using machine learning methods and the problem is modeled under uncertainties by information-gap decision theory (IGDT). This method doesn't require probability distribution functions of uncertain variables which makes it valuable in cases with high levels of uncertainties or lack of sufficient historical data. The advantage of flexibility in increasing robustness is studied by adjusting desired indoor and hot water temperatures. The effectiveness and efficiency of the proposed model are evaluated on the LEC at Chalmers University of Technology campus, Gothenburg, Sweden.
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| 11016. |
- Mohiti Ardakani, Maryam, 1989, et al.
(författare)
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An IGDT-Based Energy Management System for Local Energy Communities Considering Phase-Change Thermal Energy Storage
- 2024
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Ingår i: 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022. - 1939-9367 .- 0093-9994. ; 60:3, s. 4470-4481
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Tidskriftsartikel (refereegranskat)abstract
- Local energy communities (LECs) facilitate energy distribution, supply, consumption, storage, and trading for the communities and their members. This paper proposes an energy management system (EMS) for optimal heat and power scheduling in LECs. A novel model for the phase-change thermal energy storage (TES) which is applicable in mixed integer linear problems (MILP), is introduced. Furthermore, a risk-averse and risk-seeker EMS is developed that incorporates the integration of TES to optimize electricity and heat scheduling in LECs. The developed EMS doesn't require probability distribution functions of predicted data which makes it valuable in cases with high levels of uncertainties or lack of sufficient historical data. To validate the performance of the proposed TES model, real time studies are conducted on an industrial TES provided by Azelio company. Likewise, the effectiveness and efficiency of the proposed EMS are evaluated on a real LEC at Chalmers University of Technology campus, Gothenburg, Sweden.
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| 11017. |
- Mohseni, Farzad, et al.
(författare)
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Biogas from renewable electricity : Increasing a climate neutral fuel supply
- 2012
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 90:1, s. 11-16
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Tidskriftsartikel (refereegranskat)abstract
- If considering the increased utilisation of renewable electricity during the last decade, it is realistic to assume that a significant part of future power production will originate from renewable sources. These are normally intermittent and would cause a fluctuating electricity production. A common suggestion for stabilising intermittent power in the grid is to produce hydrogen through water electrolysis thus storing the energy for later. It could work as an excellent load management tool to control the intermittency, due to its flexibility. In turn, hydrogen could be used as a fuel in transport if compressed or liquefied. However, since hydrogen is highly energy demanding to compress, and moreover, has relatively low energy content per volume it would be more beneficial to store the hydrogen chemically attached to carbon forming synthetic methane (i.e. biogas). This paper presents how biogas production from a given amount of biomass could be increased by addition of renewable electricity. Commonly biogas is produced through digestion of organic material. Recently also biomass gasification is gaining more attention and is under development. However, in both cases, a significant amount of carbon dioxide is produced as by-product which is subject for separation and disposal. To increase the biogas yield, the separated carbon dioxide (which is considered as climate neutral) could, instead of being seen as waste, be used as a component to produce additional methane through the well-known Sabatier reaction. In such process the carbon could act as hydrogen carrier of hydrogen originating from water electrolysis driven by renewable sources. In this study a base case scenario, describing biogas plants of typical sizes and efficiencies, is presented for both digestion and gasification. It is assessed that, if implementing the Sabatier process on gasification, the methane production would be increased by about 110%. For the digestion, the increase, including process improvements, would be about 74%. Hence, this method results in greatly increased biogas potential without the addition of new raw material to the process. Additionally, such model would present a great way to meet the transport sector's increasing demand for renewable fuels, while simultaneously reducing net emissions of carbon dioxide.
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| 11018. |
- Mohseni, Farzane, et al.
(författare)
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Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:18
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Tidskriftsartikel (refereegranskat)abstract
- SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.
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| 11019. |
- Mohseni, Farzane, et al.
(författare)
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Global Soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm
- 2023
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Ingår i: Global and Planetary Change. - 1872-6364. ; 231
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Tidskriftsartikel (refereegranskat)abstract
- The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources, droughts and floods, and desertification. This study aimed to detect and map the global SMC change using microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and nonlinear trends were observed in the global land areas that have experienced statistically significant changes. The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20% agreement, demonstrating the robustness and reliability of the observed trends.
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| 11020. |
- Mohseni, Farzane, et al.
(författare)
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Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine
- 2023
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Ingår i: Remote Sensing. - 2072-4292. ; 15:14
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Tidskriftsartikel (refereegranskat)abstract
- The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).
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