Article
Thermodynamics
Wenjun Tang, Hao Wang, Xian-Long Lee, Hong-Tzer Yang
Summary: This study uses machine learning to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on smart meter data. The findings reveal the diversity of load patterns and the difference between weekdays and weekends, and suggest that age and education level may influence load patterns. The proposed analytical model using feature selection and machine learning proves to be more effective in mapping the relationship between load patterns and socioeconomic features than XGBoost and conventional neural network models.
Article
Energy & Fuels
Nadia Nedjah, Kleber Hochwart Cardoso, Luiza Macedo Mourelle
Summary: This study proposes a distributed implementation of self-healing mechanism in smart grids, which can quickly find satisfactory reconfiguration solutions and enhance network intelligence. The results show that this implementation performs significantly better than the expected upper bounds in terms of reconfiguration time and communication cost, achieving substantial speedup in cases of single and multiple failures.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Joao C. Sousa, Hermano Bernardo
Summary: As household smart meters become more common in developed countries, the consumption data they provide is playing a crucial role in the energy sector. This study used data from the London Households dataset to forecast the day-ahead load profile based on previous load values and auxiliary variables. Different forecasting models, including Multivariate Adaptive Regression Splines, Random Forests, and Artificial Neural Networks, were tested and compared. The results showed that the forecasting models were effective, with a mean reduction of 15% in Mean Absolute Error compared to the baseline. Artificial Neural Networks were found to be the most accurate model for the majority of residential consumers.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Rui Tang, Jonathon Dore, Jin Ma, Philip H. W. Leong
Summary: This study introduces an interpolation model based on SRGAN to generate higher resolution PV and load power data from low resolution data, improving accuracy in modeling and optimization of PV-integrated battery systems. Results from validation and comparison show that the model can effectively capture the targeted data features and demonstrate consistency across different scenarios.
Article
Chemistry, Multidisciplinary
Fatih Unal, Abdulaziz Almalaq, Sami Ekici
Summary: Short-term load forecasting models are crucial for distribution companies to make effective decisions, especially when forecasting load profiles of many end-users at the customer-level which faces challenges such as high variability and uncertainty. The novel hybrid deep learning approach for energy consumption prediction outperforms traditional prediction models, showing higher accuracy and robustness.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Interdisciplinary Applications
Michael Segal, Oren Tzfaty
Summary: The bounded-diameter minimum spanning tree problem seeks to find a minimum weight spanning tree on a connected, weighted, undirected graph G with a diameter no more than D. A new algorithm has been developed that can handle graphs with non-negative weights and has been proven to have a certain performance ratio. The algorithm's performance has been evaluated empirically as well.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaojun Zhu, Shaojie Tang
Summary: Minimum load spanning tree (MLST) problem plays a significant role in various applications, such as wireless sensor networks (WSNs). In this paper, the first exact algorithms for the MLST problem are proposed, which not only have theoretical guarantees but also exhibit extraordinary performance in practice. The results of the proposed algorithms contribute significantly to the fields of graph theory, internet of things, and WSNs.
INFORMS JOURNAL ON COMPUTING
(2021)
Review
Energy & Fuels
Prajowal Manandhar, Hasan Rafiq, Edwin Rodriguez-Ubinas
Summary: Urban energy modeling plays a crucial role in planning and efficiently managing electric power systems. Electricity load forecasts are important for estimating load demand and aiding power system operation. This article reviews recent literature on data-driven electricity load forecasts, addressing the factors affecting accuracy, reviewing forecasting techniques, and highlighting challenges and proposed improvements.
Article
Multidisciplinary Sciences
Lucas Pereira, Donovan Costa, Miguel Ribeiro
Summary: Smart meter data is crucial for next-generation power grids and machine learning algorithms. The SustDataED2 dataset described in this paper provides aggregated and individual appliance consumption data, as well as timestamps for evaluating machine learning problems.
Article
Energy & Fuels
Ding Han, Hongkun Bai, Yuanyuan Wang, Feifei Bu, Jian Zhang
Summary: This paper proposes a day-ahead load forecasting approach that uses smart meter data aggregated by residential customers' power consumption characteristics. The approach improves forecasting accuracy by identifying specific load patterns for each consumer type. The method involves extracting long-term trend and daily fluctuation information, clustering residential consumers using the K-means algorithm, and forecasting each cluster's load patterns using a non-linear autoregressive neural network.
Article
Chemistry, Multidisciplinary
Daniel D. Campo-Ossa, Cesar A. Vega A. Penagos, Oscar D. Garzon, Fabio Andrade
Summary: This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. Real data, synthetic data, and electricity bill data were collected to validate and improve the load profile models. The solar-rooftop energy potential was estimated using the PVWatts calculator and mathematical analysis, enabling the determination of the minimum size of solar power systems to meet the energy demand in the community.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Cesar Rego, Frank Mathew
Summary: We develop a scatter search algorithm to solve the classical capacitated minimum spanning tree problem, including both homogeneous and heterogeneous variants. This problem is central in network design applications in industrial engineering, routing and logistics, and communication networks. Since it is an NP-Complete problem, heuristic solution methods are necessary to find high-quality solutions within practical time limits. Our proposed algorithm competes with the best algorithms in the literature and avoids complicated artifacts.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Mathematics
Zhuoran Wang, Dian Ouyang, Yikun Wang, Qi Liang, Zhuo Huang
Summary: Computing directed Minimum Spanning Tree (DMST) is a fundamental problem in graph theory, applied in various fields such as computer network, communication protocol design, revenue maximization in social networks, and syntactic parsing in Natural Language Processing. This paper proposes an indexed approach that reuses computation results for single and batch queries of DMST, achieving significant speedup while consuming minimal index size.
Article
Construction & Building Technology
Hasan Rafiq, Prajowal Manandhar, Edwin Rodriguez-Ubinas, Juan David Barbosa, Omer Ahmed Qureshi
Summary: Analyzing residential load profiles and usage patterns is crucial for demand-side management and energy-saving strategies. This paper presents detailed research on electricity consumption and profiles in Dubai based on dwellings' characteristics and smart meter data. The households were grouped using K-Means clustering, and consumption patterns were organized and identified. Classification algorithms were applied to predict household patterns based on characteristics. The analysis revealed peak demand variations and identified key characteristics driving electricity demand patterns.
ENERGY AND BUILDINGS
(2023)
Article
Chemistry, Analytical
Omar Munoz, Adolfo Ruelas, Pedro Rosales, Alexis Acuna, Alejandro Suastegui, Fernando Lara
Summary: This paper presents the design, construction, and validation of a smart meter with load control to address the issue of rising electricity consumption. The meter not only monitors energy consumption but also provides additional parameters, and its accuracy was proven through experimentation. The real-life application of the device was also demonstrated.
Article
Multidisciplinary Sciences
Marko Gosak, Moritz U. G. Kraemer, Heinrich H. Nax, Matjaz Perc, Bary S. R. Pradelski
Summary: This study emphasizes the importance of social distancing in mitigating the impact of infectious diseases, particularly during a pandemic. Using game theory, the interaction of voluntary social distancing in a partially infected population can be formalized to predict differential social distancing rates dependent on health status. The findings suggest that realistically benchmarking epidemic models considering endogenous social distancing is crucial for informing social distancing orders and lockdown policies.
SCIENTIFIC REPORTS
(2021)
Article
Physics, Multidisciplinary
Marko Gosak, Maja Duh, Rene Markovic, MatjaZ Perc
Summary: Research indicates that community lockdowns are effective only when links outside the communities are virtually completely sealed off, with targeted lockdowns yielding better results than random lockdowns. However, even with over 90% lockdown effectiveness, the peak of the infected curve decreases by only 20% and its onset is delayed by a factor of 1.5.
NEW JOURNAL OF PHYSICS
(2021)
Article
Biochemical Research Methods
Marko Sterk, Lidija Krizancic Bombek, Masa Skelin Klemen, Marjan Slak Rupnik, Marko Marhl, Andraz Stozer, Marko Gosak
Summary: Our study demonstrates that NMDA receptor inhibition enhances and synchronizes beta cell activity, as well as stabilizes intercellular calcium wave propagation within the islets of Langerhans. This finding highlights the potential of NMDA receptors as a therapeutic target for diabetes and introduces the multilayer network paradigm as a useful strategy to investigate drug effects on connectivity in multicellular systems.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Cell Biology
Jan Zmazek, Vladimir Grubelnik, Rene Markovic, Marko Marhl
Summary: The computational alpha cell model showed that increased catabolic activity can suppress the cAMP signaling pathway, thereby reducing glucagon granule exocytosis. It was also found that both cAMP-driven signaling switch and ATP-driven metabolic switch play decisive roles in glucagon secretion, with a ratio of approximately 60:40 favoring the former according to recent experimental evidence.
Article
Biology
Marko Gosak, Dajana Gojic, Elena Spasovska, Marko Hawlina, Sofija Andjelic
Summary: The progression of cataract is associated with modifications in Ca2+ signaling in lens epithelial cells (LECs), with more severe cataracts leading to faster propagation of Ca2+ waves but lower amplitudes and longer durations of Ca2+ signals. However, there were no significant differences in Ca2+ signaling between different types of cataracts.
Article
Endocrinology & Metabolism
Andrai Stozer, Masa Skelin Klemen, Marko Gosak, Lidija Kriiancic Bombek, Viljem Pohorec, Marjan Slak Rupnik, Jurij Dolensek
Summary: This study investigates the concentration-dependent changes in coupled beta cells in response to glucose, the level of functional heterogeneity, and the presence of specialized subpopulations during different glucose concentrations. The findings demonstrate that as glucose concentration increases, the characteristics of islet activity change and functional connectivity evolves accordingly.
AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM
(2021)
Article
Materials Science, Multidisciplinary
Rene Markovic, Marko Sterk, Marko Marhl, Matjaz Perc, Marko Gosak
Summary: A study on an epidemiological model in a social network suggests that factors such as individual health, age, and geographical location play vital roles in the spread and outcomes of epidemics. For COVID-19, prioritizing vaccination for elderly and high-risk groups is beneficial only when vaccine supply is high; otherwise, vaccinating healthy individuals first can achieve better epidemic outcomes.
RESULTS IN PHYSICS
(2021)
Article
Health Care Sciences & Services
Rene Markovic, Vladimir Grubelnik, Helena Blazun Vosner, Peter Kokol, Matej Zavrsnik, Karmen Jansa, Marjeta Zupet, Jernej Zavrsnik, Marko Marhl
Summary: This study analyzed a large amount of laboratory data and found that the population with elevated blood glucose occurs approximately 20 years later than the population with dysregulated lipids. Two distinct inflection points were observed, corresponding to the increased medication use and the sharp increase in mortality rate.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Mathematical & Computational Biology
David Ristic, Marko Gosak
Summary: This study investigates how coherence resonance manifests in populations of excitatory and inhibitory neurons. The results reveal that the regularity of simulated neural activity can be increased by enhancing the damping effect in the excitatory layer.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2022)
Article
Biochemistry & Molecular Biology
Jan Zmazek, Vladimir Grubelnik, Rene Markovic, Marko Marhl
Summary: Type 2 Diabetes Mellitus (T2DM) is a burdensome problem in modern society, and intensive research is focused on understanding the underlying cellular mechanisms of hormone secretion for blood glucose regulation. A mathematical model has been developed that quantitatively estimates the importance of glutamine in promoting glucagon secretion, which aligns with experimental data.
Article
Biophysics
Maja Duh, Kristijan Skok, Matjaz Perc, Andrej Markota, Marko Gosak
Summary: This research investigates the differences in outcomes of targeted temperature management in post-cardiac arrest patients by using computational modeling. The study considers specific conditions related to thermal imbalances, increased metabolic rates, and various external cooling techniques. The findings provide a better understanding of heat transfer processes and therapies used in post-cardiac arrest patients.
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
(2022)
Review
Biology
Marko Gosak, Marko Milojevic, Maja Duh, Kristijan Skok, Matjaz Perc
Summary: Researchers review the advances in applying methods of network science to study the morphology and structural design of living systems. They focus on networks between cells, multicellular structures, neural interactions, fluid transportation networks, and anatomical networks. They argue that the models and algorithms developed in network science are ushering in a new era of research into living systems and emphasize the increasing importance of this research due to developments in bioartificial substitutes and tissue engineering.
PHYSICS OF LIFE REVIEWS
(2022)
Article
Multidisciplinary Sciences
Urska Marolt, Eva Paradiz Leitgeb, Viljem Pohorec, Saska Lipovsek, Viktoria Venglovecz, Eleonora Gal, Attila Ebert, Istvan Menyhart, Stojan Potrc, Marko Gosak, Jurij Dolensek, Andraz Stozer
Summary: The physiology and pathophysiology of the exocrine pancreas are closely related to changes in intracellular calcium concentration. The acute pancreas tissue slice technique is an effective method for evaluating the morphology and physiology of the pancreas in situ. This study used calcium imaging to characterize the responses of acinar cells to stimulation and compared the effects of different stimuli.
Article
Mathematics, Applied
Uros Barac, Matjaz Perc, Marko Gosak
Summary: We investigate collective failures in biologically realistic networks with coupled excitable units using the FitzHugh-Nagumo model. We examine different factors such as coupling strength, bifurcation distances, and aging scenarios that contribute to collective failure. Our findings show that targeting high-degree nodes for inactivation leads to the longest global activity in the network, consistent with previous results. However, we also demonstrate that the most efficient strategy for collective failure depends on both coupling strength and the distance from the bifurcation point to oscillatory behavior.
Article
Biochemistry & Molecular Biology
Andrej Dobovisek, Marko Vitas, Tina Blazevic, Rene Markovic, Marko Marhl, Ales Fajmut
Summary: The self-organization of enzymatic reactions in open reaction systems is studied in relation to kinetic reaction mechanisms and entropy export to the environment. Maximum entropy production principle (MEPP) is used as a theoretical framework for analysis. The results show that the optimal enzyme performance depends on the number of reaction steps and that simple reaction mechanisms with fewer steps may be more internally organized and allow faster and more stable catalysis, possibly being features of the evolutionary mechanisms of highly specialized enzymes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
