Article
Chemistry, Analytical
Habib Ullah Manzoor, Ahsan Raza Khan, David Flynn, Muhammad Mahtab Alam, Muhammad Akram, Muhammad Ali Imran, Ahmed Zoha
Summary: This paper presents FedBranched, a clustering-based framework for federated learning that addresses the challenges arising from high diversity in client data. The framework utilizes probabilistic methods and hidden Markov model clustering to create branches and assign global models. Experimental results on short-term load forecasting demonstrate the effectiveness of FedBranched in improving performance.
Article
Green & Sustainable Science & Technology
Komal Tripathi, Vrinda Gupta, Varsha Awasthi, Kamal Kishore Pant, Sreedevi Upadhyayula
Summary: This study develops an ultrafast machine learning (ML) based framework to predict CO2 conversion and methanol selectivity by extracting comprehensive knowledge from existing published literature. Among various ML algorithms, artificial neural networks (ANNs) exhibit the best accuracy. The efficacy and fidelity of the developed neural networks are depicted by satisfactory performance for majority of unseen test datasets. This work concludes that ML-based concept allows to uncover catalytic property-performance correlations hidden in existing experimental research.
ADVANCED SUSTAINABLE SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Nicoletta Matera, Domenico Mazzeo, Cristina Baglivo, Paolo Maria Congedo
Summary: Nowadays, there is great emphasis on the use of photovoltaic (PV) systems to address the issues of climate change and the energy crisis. The study demonstrates the training and validation of a network of artificial neural networks (ANNs) to predict the hourly electrical power output of different PV modules worldwide. The ANNs accurately describe the performance of each PV module under optimal inclination angles.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Article
Green & Sustainable Science & Technology
Sheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, Nadeem Javaid, Nouman Ashraf, Shahzad Aslam
Summary: Microgrids combining renewable energy sources, energy storage devices, and load management methods face challenges due to the intermittent nature of renewables. Forecasting power generation from renewables is crucial for efficient grid operations and optimal resource utilization. Machine learning and deep learning models show promise in predicting energy demand and generation, with the efficiency of forecasting methods depending on historical data availability.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Theory & Methods
Aristeidis Mystakidis, Evangelia Ntozi, Konstantinos Afentoulis, Paraskevas Koukaras, Paschalis Gkaidatzis, Dimosthenis Ioannidis, Christos Tjortjis, Dimitrios Tzovaras
Summary: Distribution System Operators (DSOs) and Aggregators can benefit from improved Energy Generation Forecasting (EGF) approaches, which help deal with energy imbalances and support Demand Response (DR) management in Smart Grid architecture. This study aims to develop and test a new EGF solution by combining various methodologies and evaluating their performance using historical building data. The final forecasting evaluation includes performance metrics such as R-2, MAE, MSE, and RMSE to provide a comparative analysis of the results.
Review
Green & Sustainable Science & Technology
Putri Nor Liyana Mohamad Radzi, Muhammad Naveed Akhter, Saad Mekhilef, Noraisyah Mohamed Shah
Summary: Advancements in renewable energy technology have reduced consumer dependence on conventional energy sources. Solar energy is a sustainable source of power generation with various factors affecting its performance. Machine learning and neural networks play a crucial role in accurately forecasting the output power of photovoltaic systems, taking into account different input parameters and time-step resolutions.
Article
Energy & Fuels
Changsu Kim, Jiyong Kim
Summary: In this study, four different ANNs were used for predicting the performance of Pt-based catalysts in water gas shift reaction, with the multilayer perceptron model showing the best performance. It was demonstrated how selecting the optimal ANN structure can improve prediction accuracy and reduce computational load.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Engineering, Environmental
Mustafa El-Rawy, Mahmoud Khaled Abd-Ellah, Heba Fathi, Ahmed Khaled Abdella Ahmed
Summary: This study introduces two methods for predicting and forecasting the removal efficiency of pollutants in wastewater treatment plants: traditional feed-forward, deep feed-forward backpropagation, and deep cascade-forward backpropagation networks; and deep learning time series forecasting with a long short-term memory network. The results show that the DCB network has the highest accuracy and is recommended for evaluating and predicting WWTP performance.
JOURNAL OF WATER PROCESS ENGINEERING
(2021)
Article
Computer Science, Information Systems
K. U. Jaseena, Binsu C. Kovoor
Summary: Weather forecasting is the practice of predicting the state of the atmosphere based on different weather parameters. Accurate weather forecasts are crucial in various fields. With the advancement of atmospheric observing systems and the increasing volume of weather data, deep learning techniques are being used to improve weather prediction. This paper provides a comprehensive review of weather forecasting approaches and discusses potential future research directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
M. O. Moreira, P. P. Balestrassi, A. P. Paiva, P. F. Ribeiro, B. D. Bonatto
Summary: This article discusses a method for forecasting photovoltaic generation using a new approach based on an artificial neural network (ANN) ensemble. By utilizing a design of experiments (DOE) approach and cluster analysis, the best networks are selected, and a mixture (MDE) is used to determine ideal weights for ensemble formation. This methodology allows for flexibility in the experimental arrangement, forecast model, and desired forecast horizon, enhancing forecasting accuracy.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Energy & Fuels
Jelena Simeunovic, Baptiste Schubnel, Pierre-Jean Alet, Rafael E. Carrillo, Pascal Frossard
Summary: This paper proposes a new temporal-spatial multi-windows graph attention network (TSM-GAT) for predicting future PV power production by representing PV systems as nodes of a dynamic graph. TSM-GAT adapts to the dynamics of the problem and outperforms other models for four to six hours ahead predictions. It also outperforms state-of-the-art models that use NWP as inputs.
Review
Environmental Sciences
Muhammad Waqas, Usa Wannasingha Humphries, Angkool Wangwongchai, Porntip Dechpichai, Shakeel Ahmad
Summary: Rainfall forecasting is crucial for Thailand's agricultural sector, and artificial intelligence techniques have shown remarkable precision in this field. This research investigates the most recent artificial intelligence techniques, such as advanced machine learning, artificial neural networks, and deep learning, used for rainfall forecasting in Thailand.
Article
Computer Science, Information Systems
Giambattista Gruosso, Giancarlo Storti Gajani
Summary: The availability of reliable photovoltaic power forecasting tools is crucial for the dissemination of this technology. Edge computing can localize and make predictions feasible with the use of small, low power, and inexpensive devices. This article explores prediction methods based on Artificial Neural Networks (ANNs) models, and investigates techniques to reduce their cost. The aging effects of solar panels are also considered.
Article
Computer Science, Information Systems
Georgios Tziolis, Andreas Livera, Jesus Montes-Romero, Spyros Theocharides, George Makrides, George E. Georghiou
Summary: This study presents a machine learning-based approach for short-term net load forecasting in solar-integrated microgrids. The proposed model demonstrates accurate and robust prediction performance, validated using historical net load data. The results show a 17.77% improvement over baseline models.
Article
Engineering, Electrical & Electronic
Mostafa Al-Gabalawy, Nesreen S. Hosny, Ahmed R. Adly
Summary: This paper investigates the application of deep learning algorithms in energy time series forecasting and discusses methods to evaluate their confidence. Comparing different algorithms in load forecasting scenarios, it is found that Concrete Dropout, Deep Ensembles, and Bayesian Neural Networks perform well, with advantages in speed and convergence time.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Spyros Theocharides, Chrysovalantis Spanias, Ioannis Papageorgiou, George Makrides, Stavros Stavrinos, Venizelos Efthymiou, George E. Georghiou
Summary: Accurate PV power forecasting is essential for integrating solar electricity efficiently. This study proposes a novel method for providing day-ahead aggregated PV production forecasts for distributed PV systems. By using clustering and up-scaling, the aggregated PV generation was successfully estimated.
IET RENEWABLE POWER GENERATION
(2022)
Article
Green & Sustainable Science & Technology
Irene Romero-Fiances, Andreas Livera, Marios Theristis, George Makrides, Joshua S. Stein, Gustavo Nofuentes, Juan de la Casa, George E. Georghiou
Summary: Accurate quantification of photovoltaic system degradation rate is essential for lifetime yield predictions. Different techniques applied to synthetic PV system data showed varying levels of accuracy, affected by evaluation duration and missing data. Filtering out corrupted data and applying a change-point detection stage are necessary for accurate estimation of degradation rate.
Article
Energy & Fuels
Marios Theristis, Joshua S. Stein, Chris Deline, Dirk Jordan, Charles Robinson, William Sekulic, Allan Anderberg, Dylan J. Colvin, Joseph Walters, Hubert Seigneur, Bruce H. King
Summary: The study investigates the cost reduction of photovoltaic modules since 2010 and examines if the changes in module designs and materials have affected module durability. The research finds that while degradation rates are nonlinear over time and seasonal variations exist in some modules, the overall degradation rates are similar to older modules. The study also identifies that some systems may exceed warranty limits in the future, but others demonstrate the potential to achieve lifetimes beyond 30 years.
PROGRESS IN PHOTOVOLTAICS
(2023)
Article
Energy & Fuels
Andreas Livera, Georgios Tziolis, Jose G. Franquelo, Ruben Gonzalez Bernal, George E. Georghiou
Summary: This paper proposes a cloud-based platform for reducing PV operation and maintenance costs and improving lifetime performance. The platform incorporates a decision support system (DSS) engine and data-driven functionalities to ensure optimum performance by monitoring the operating state of PV assets in real time and providing recommendations for resolving underperformance issues.
Article
Energy & Fuels
Stylianos Loizidis, Georgios Konstantinidis, Spyros Theocharides, Andreas Kyprianou, George E. Georghiou
Summary: Participants in deregulated electricity markets face risks from various factors, and price forecasting using machine learning techniques is used to mitigate these risks. This study compares the performance of different algorithms, including Extreme Learning Machine, Artificial Neural Network, XGBoost, and random forest, in the Day-Ahead markets of Germany and Finland. The findings show that random forest performs best for normal and extremely high prices, while XGBoost is more effective for negative prices.
Article
Energy & Fuels
Nikolas G. Chatzigeorgiou, Spyros Theocharidis, George Makrides, George E. Georghiou
Summary: This study aims to evaluate the sizing and techno-economic aspects of residential PV-BSS systems, considering different supporting schemes. The results show that the sizing of PV-BSS is significantly influenced by the supporting scheme, and different schemes have a significant impact on the maximum financial gains.
Article
Energy & Fuels
Andreas Livera, Georgios Tziolis, Marios Theristis, Joshua S. Stein, George E. Georghiou
Summary: Accurate quantification of the performance loss rate of photovoltaic systems is crucial, and this study compares common change point methods for estimating performance loss rate. Through an extensive testing campaign using historical electrical data and meteorological measurements over 8 years from 11 photovoltaic systems in Nicosia, Cyprus, the study evaluates time series analysis approaches. The results highlight the importance of applying nonlinear techniques and extracting robust nonlinear models to accurately detect significant changes and estimate performance loss rate.
Article
Energy & Fuels
Venizelos Venizelou, Apostolos C. Tsolakis, Demetres Evagorou, Christos Patsonakis, Ioannis Koskinas, Phivos Therapontos, Lampros Zyglakis, Dimosthenis Ioannidis, George Makrides, Dimitrios Tzovaras, George E. Georghiou
Summary: Unlocking flexibility on the demand side is necessary to balance supply and demand in high-penetration renewable energy distribution networks. Demand response schemes through aggregation are key to accessing this flexibility. This study introduces a holistic demand response framework that optimizes the aggregator's strategies for flexible dispatch while cooperating with the distribution system operator. The framework is evaluated on a modified IEEE 33-Bus radial distribution system and successfully executes a real demand response event without disrupting grid operation.
Article
Thermodynamics
Georgios Tziolis, Chrysovalantis Spanias, Maria Theodoride, Spyros Theocharides, Javier Lopez-Lorente, Andreas Livera, George Makrides, George E. Georghiou
Summary: A new methodology based on a Bayesian neural network model was proposed for direct short-term net load forecasting at the distribution level. The optimized model achieved high forecasting accuracies, and the statistical post-processing further improved the accuracy. The results demonstrated the suitability of the methodology for distribution feeders with diverse PV penetration shares.
Article
Energy & Fuels
Marios Theristis, Nicholas Riedel-Lyngskaer, Joshua S. S. Stein, Lelia Deville, Leonardo Micheli, Anton Driesse, William B. B. Hobbs, Silvana Ovaitt, Rajiv Daxini, David Barrie, Mark Campanelli, Heather Hodges, Javier R. R. Ledesma, Ismael Lokhat, Brendan McCormick, Bin Meng, Bill Miller, Ricardo Motta, Emma Noirault, Megan Parker, Jesus Polo, Daniel Powell, Rodrigo Moreton, Matthew Prilliman, Steve Ransome, Martin Schneider, Branislav Schnierer, Bowen Tian, Frederick Warner, Robert Williams, Bruno Wittmer, Changrui Zhao
Summary: The PVPMC organized a blind PV performance modeling intercomparison to test the models and modeling ability of PV modelers using real system data. Participants simulated the plane-of-array irradiance, module temperature, and DC power output from six systems and submitted their results for processing. The results showed some errors due to human errors, modeling skills, and derates, but overall exhibited improved precision and accuracy compared to a previous study in 2010.
PROGRESS IN PHOTOVOLTAICS
(2023)
Article
Energy & Fuels
Michael G. Deceglie, Kevin Anderson, Daniel Fregosi, William B. Hobbs, Mark A. Mikofski, Marios Theristis, Bennet E. Meyers
Summary: Photovoltaic systems may perform below expectations due to inaccurate estimates, suboptimal operations and maintenance, or component degradation. Accurate assessment of loss factors helps address underperformance and achieve accurate expectations and models. It is important to distinguish between recoverable and nonrecoverable losses underlying performance loss rate (PLR).
Article
Computer Science, Information Systems
Georgios Tziolis, Andreas Livera, Jesus Montes-Romero, Spyros Theocharides, George Makrides, George E. Georghiou
Summary: This study presents a machine learning-based approach for short-term net load forecasting in solar-integrated microgrids. The proposed model demonstrates accurate and robust prediction performance, validated using historical net load data. The results show a 17.77% improvement over baseline models.
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.