Article
Thermodynamics
Md Tanvir Ahammed, Imran Khan
Summary: The increase in population and adoption of new power appliances has led to a significant rise in electrical demands, causing challenges for utilities in maintaining supply-demand balance and resulting in issues like load-shedding and power quality drops, particularly in developing countries. By implementing efficient demand-side management algorithms in metering instruments, it is possible to mitigate these issues and increase consumer awareness of energy consumption. Introducing IoT-based smart meters can provide solutions for monitoring, bidirectional data transmission, and load-clipping, helping to maintain power quality parameters within standard limits and improve the balance between supply and demand.
Article
Energy & Fuels
Hyojeoung Kim, Sujin Park, Sahm Kim
Summary: This study presents a bottom-up prediction method for electricity consumption in a smart grid environment using time-series clustering and AMI data. The time-series clustering method outperforms the total amount of electricity demand in terms of forecast accuracy. Various models were used for demand forecasting based on clustering, and the model considering exogenous variables performed better than the one without exogenous variables.
Article
Chemistry, Multidisciplinary
Md Mahraj Murshalin Al Moti, Rafsan Shartaj Uddin, Md Abdul Hai, Tanzim Bin Saleh, Md Golam Rabiul Alam, Mohammad Mehedi Hassan, Md Rafiul Hassan
Summary: This research proposes a blockchain-based electricity marketplace for the smart grid environment, introducing a decentralized ledger for trust and traceability among stakeholders. The use of the Stackelberg model and reinforcement learning enables dynamic price forecasting, optimizing the smart grid system.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Hardware & Architecture
Silvio E. Quincozes, Celio Albuquerque, Diego Passos, Daniel Mosse
Summary: Smart Grids integrate the traditional power grid with information processing and communication technologies, with substation intelligent devices now communicating digitally for remote information gathering, monitoring, and control. The IEC-61850 international standard addresses substation communication networks and systems, but standardized communication brings new cyber-security challenges. Research focuses on analyzing attacks exploiting IEC-61850 substations and efforts to detect and prevent them.
Article
Energy & Fuels
Ciaran Gilbert, Jethro Browell, Bruce Stephen
Summary: Short-term forecasts of energy consumption are crucial for energy systems to function properly, especially for low voltage electricity networks. However, predicting network loads becomes challenging when there are only a few customers with highly disaggregated loads, as individual behaviors dominate rather than smooth aggregate consumption patterns. In addition, distribution networks face challenges mostly from peak loads, and tasks like storage scheduling and demand flexibility rely on accurate predictions of peak demand, which is often not well characterized by general-purpose forecasting methods. In this study, we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure to combine conventional and peak forecasts, resulting in a probabilistic forecast with improved performance during peak hours. The effectiveness of this approach is demonstrated using real smart meter data and a hypothetical low voltage network. The fusion of state-of-the-art probabilistic load forecasts with peak forecasts significantly improves overall performance, especially at smart-meter and feeder levels, with a greater than 10% improvement in terms of CRPS.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Construction & Building Technology
Imran Khan
Summary: Demand side management (DSM) is a useful approach to reduce electricity peak demand, typically utilizing smart meters in developed countries. However, lack of smart technologies and detailed information in developing countries have hindered the implementation of DSM schemes. This study proposes a survey-based method for demand profiling, particularly for developing countries like Bangladesh, which could provide more accurate information during peak demand hours and help design proper DSM strategies.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
S. M. Sulaiman, P. Aruna Jeyanthy, D. Devaraj, K. Shihabudheen
Summary: A novel hybrid method based on EMD and ELM is proposed in this paper to improve the forecast accuracy of residential load signals derived from Smart Meter data. The results show that the proposed method is effective in capturing the peaks present in residential loads and improves the forecast accuracy. Comparison with other machine learning methods demonstrates the superiority of the proposed method.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Energy & Fuels
Anshul Agarwal, Kedar Khandeparkar
Summary: A rapid rise in power consumption, particularly in commercial and residential buildings, has led to an imbalance compared to power generation. To address this issue, the paper proposes a systematic brownout mechanism and develops algorithms for threshold distribution to ensure fairness and minimize violations.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
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
Energy & Fuels
Joao Victor Jales Melo, George Rossany Soares Lira, Edson Guedes Costa, Antonio F. Leite Neto, Iago B. Oliveira
Summary: This study aims to implement a load predictive model focused on individual consumers and reduce forecasting errors by selecting appropriate features. Among the analyzed techniques, support vector regression (SVR) showed the smallest errors in the prediction of individual consumer load.
Article
Engineering, Multidisciplinary
Mehmet Gucyetmez, Husham Sakeen Farhan
Summary: The traditional electricity grid needs to be transformed into a smart grid infrastructure to address the increasing electricity demand and energy prices. Smart meters play a vital role in this transformation by enabling consumers to track their energy consumption and receive warnings. The developed Internet of Things (IoT) based smart meter has features like high data rate, bidirectional data transmission, and integration with fuzzy system and mobile application software.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
D. Kiruthiga, V Manikandan
Summary: Accurate short-term load forecasting is essential for improving power systems, and researchers have developed a novel deep learning framework to enhance prediction accuracy, as well as utilizing hierarchical clustering technique to improve load forecasting accuracy.
ELECTRICAL ENGINEERING
(2022)
Review
Chemistry, Analytical
Javier Manuel Aguiar-Perez, Maria Angeles Perez-Juarez
Summary: Smart grids can forecast customers' energy demand and transmit electricity accordingly by considering the expected demand. To tackle the challenges of demand forecasting with large amounts of data generated by smart grids, modern data-driven techniques are needed. Among these techniques, Long Short-Term Memory networks based on Recurrent Neural Networks are widely used for learning patterns from customer data and predicting demand for various time horizons. This paper emphasizes the importance of demand forecasting and related factors in the context of smart grids, and shares experiences of using Deep Learning techniques for this purpose.
Review
Computer Science, Artificial Intelligence
Alberto Gasparin, Slobodan Lukovic, Cesare Alippi
Summary: This study compares and experimentally evaluates different deep learning architectures for electric load forecasting, focusing on short-term predictions (one-day-ahead). The research examines feedforward and recurrent neural networks, sequence-to-sequence models, and temporal convolutional neural networks in the context of four real-world datasets.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Ming Yi, Meng Wang
Summary: This article introduces a Bayesian-dictionary-learning-based approach for energy disaggregation at substations, which improves disaggregation accuracy by learning the probability distributions of load patterns and decomposition coefficients, and provides an uncertainty measure of the estimation.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Georgios Giasemidis, Nikolaos Kaplis, Ioannis Agrafiotis, Jason R. C. Nurse
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Energy & Fuels
Feras Alasali, Stephen Haben, Husam Foudeh, William Holderbaum
Article
Engineering, Electrical & Electronic
Rory Telford, Bruce Stephen, Jethro Browell, Stephen Haben
Summary: This study proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters, which learns daily load profiles across customers with Smart Meters and applies them to unmetered customers to estimate network parameters. By comparing estimations with simulated LV network models and benchmark models, the proposed method outperforms in accurately assessing substation headroom, especially in scenarios with a lower percentage of customers having Smart Meters installed.
IEEE TRANSACTIONS ON POWER DELIVERY
(2021)
Article
Immunology
Fay Probert, Tianrong Yeo, Yifan Zhou, Megan Sealey, Siddharth Arora, Jacqueline Palace, Timothy D. W. Claridge, Rainer Hillenbrand, Johanna Oechtering, Jens Kuhle, David Leppert, Daniel C. Anthony
Summary: The study aimed to identify biomarkers complementary to CSF oligoclonal IgG bands (OCGB) to improve diagnostic accuracy in OCGB positive patients. Results showed that CCN5 levels and GFAP can improve the accuracy and specificity of MS diagnosis, and a multiomics approach further improved accuracy to 90%.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Medicine, General & Internal
Siddharth Arora, Athanasios Tsanas
Summary: This study evaluated the potential of using voice as a population-based PD screening tool and successfully differentiated PD participants from controls using dysphonia measures with 67.43% sensitivity and 67.25% specificity. These findings could contribute to the development of an inexpensive and reliable diagnostic support tool for PD.
Article
Clinical Neurology
Christine Lo, Siddharth Arora, Michael Lawton, Thomas Barber, Timothy Quinnell, Gary J. Dennis, Yoav Ben-Shlomo, Michele Tao-Ming Hu
Summary: The composite clinical motor score may offer greater consistency and sensitivity in detecting motor changes in early disease than the MDS-UPDRS III alone. It is also more accurate in predicting clinical outcomes, requiring fewer participants in sample size estimations.
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY
(2022)
Correction
Immunology
Fay Probert, Tianrong Yeo, Yifan Zhou, Megan Sealey, Siddharth Arora, Jacqueline Palace, Timothy D. W. Claridge, Rainer Hillenbrand, Johanna Oechtering, Jens Kuhle, David Leppert, Daniel C. Anthony
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Management
Siddharth Arora, James W. Taylor, Ho-Yin Mak
Summary: This study focuses on estimating the probability distribution of individual patient waiting times in an emergency department using a machine learning approach. The proposed method provides more accurate probabilistic forecasts compared to existing methods that only focus on point forecasts. This can improve overall patient satisfaction and prevent patient abandonment.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2023)
Article
Clinical Neurology
Fay Probert, Tianrong Yeo, Yifan Zhou, Megan Sealey, Siddharth Arora, Jacqueline Palace, Timothy D. W. Claridge, Rainer Hillenbrand, Johanna Oechtering, David Leppert, Jens Kuhle, Daniel C. Anthony
Summary: The study aimed to identify biomarkers that can predict the progression from clinically isolated syndrome to multiple sclerosis, and developed a multi-optics algorithm with 83% predictive accuracy by combining various biochemical and protein markers. These novel biomarkers shed light on the molecular pathways involved in disease progression and offer a more accurate way to predict the conversion to clinically defined multiple sclerosis.
BRAIN COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Marcus Voss, Jan F. Heinekamp, Sabine Krutzsch, Friedrich Sick, Sahin Albayrak, Kai Strunz
Summary: This study introduces a mathematical model of building inertia thermal energy storage (BITES) for optimized smart grid control, utilizing the inherent thermal storage capability of building mass. The model parameters are obtained using generalized additive modeling (GAM) based on measurable building data, with the ceiling surface temperature serving as a proxy for energy state. Two case studies demonstrate the potential of BITES as part of a virtual power plant (VPP), showing economic benefits compared to conventional hot water tanks. The research highlights the flexibility and benefits that BITES can offer to low-carbon energy systems in the future.
Article
Computer Science, Information Systems
Siddharth Arora, Christine Lo, Michele Hu, Athanasios Tsanas
Summary: The study aims to use smartphone speech testing to differentiate between participants with Rapid Eye Movement (REM) sleep Behavior Disorder (RBD) and Parkinson's Disease (PD), and to predict clinical scores assessing motor symptoms, cognition, daytime sleepiness, depression, and overall health. Results show promising potential for speech as a digital biomarker for early intervention in PD and RBD.
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.
