Article
Energy & Fuels
Benedikt Schulz, Mehrez El Ayari, Sebastian Lerch, Sandor Baran
Summary: Probabilistic energy forecasting is crucial for integrating volatile power sources like solar energy into the electrical grid. Hybrid models combining physical and statistical methods have shown to be effective, with post-processing models proving to significantly improve the forecast performance of ensemble predictions and correct systematic biases.
Article
Management
Nicole Ludwig, Siddharth Arora, James W. Taylor
Summary: Probabilistic forecasting of electricity demand is important for efficient management and operations of energy systems. The relationship between load and weather is complex and nonlinear, making load modeling using weather challenging. This study focuses on using weather ensemble predictions to model load in Great Britain. The ensembles are post-processed using ensemble model output statistics and ensemble copula coupling to improve their accuracy. The proposed approach outperforms other methods that do not use weather information or employ raw weather ensembles.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Geosciences, Multidisciplinary
Yan Ji, Xiefei Zhi, Luying Ji, Yingxin Zhang, Cui Hao, Ting Peng
Summary: This study used deep-learning-based models to improve probabilistic precipitation forecasting over China and found that the deep NN model outperformed traditional methods and raw ensemble in predicting extreme precipitation events. The size of the training samples was found to have a significant impact on the results, and the forecast performance was better in central China.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
Zohreh Javanshiri, Maede Fathi, Seyedeh Atefeh Mohammadi
Summary: This study explores the use of ensemble forecasting in probabilistic weather forecasting, emphasizing the importance of statistical post-processing to improve forecast quality. Results show that BMA and EMOS-CSG techniques are successful in enhancing WRF ensemble forecasts, with BMA being more accurate, skillful, and reliable, while EMOS-CSG method exhibits better resolution in predicting high-precipitation events.
METEOROLOGICAL APPLICATIONS
(2021)
Article
Thermodynamics
Myeongchan Oh, Chang Ki Kim, Boyoung Kim, Changyeol Yun, Jin-Young Kim, Yongheack Kang, Hyun-Goo Kim
Summary: This study proposes an advanced separation model for direct irradiance using machine learning techniques with 1-min time-series variabilities, trained and validated on data from over 20 stations in temperate climate regions. Results show that ML models outperform previous models at all stations, with neural networks showing the best performance.
Article
Green & Sustainable Science & Technology
Francis M. Lopes, Ricardo Conceicao, Hugo G. Silva, Rui Salgado, Manuel Collares-Pereira
Summary: This study enhances operational strategies of concentrating solar power plants by utilizing post-processing methods on numerical weather predictions to improve the accuracy of electricity forecasts. By using short-term forecasts from the Integrated Forecasting System and measurements in southern Portugal, the research significantly increases the prediction accuracy in hourly forecasts.
Article
Environmental Sciences
Luying Ji, Qixiang Luo, Yan Ji, Xiefei Zhi
Summary: Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) are used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere. Both methods provide better calibrated and sharper probability density functions compared to raw ensembles, with EMOS slightly outperforming BMA for shorter lead times and BMA performing better for longer lead times.
Article
Environmental Sciences
Dongyu Jia, Jiajia Hua, Liping Wang, Yitao Guo, Hong Guo, Pingping Wu, Min Liu, Liwei Yang
Summary: Accurate solar radiation estimation is crucial for solar energy systems, with high-precision remote sensing data being used to fill the gap in surface radiation observations. This study tested a global solar irradiance estimation method using data from the FengYun-4A satellite, showing that the estimated global horizontal irradiance (GHI) was slightly affected by seasons, while direct normal irradiance (DNI) estimation was less accurate. A new DNI radiation algorithm during autumn and winter is proposed for northern China to improve accuracy.
Article
Energy & Fuels
Adnan Ayaz, Faraz Ahmad, Mohammad Abdul Aziz Irfan, Zabdur Rehman, Krzysztof Rajski, Jan Danielewicz
Summary: With the depletion of fossil reserves, the demand for renewable energy, especially solar power plants, has reached its peak globally. In order to properly estimate the solar energy potential, a feasibility study is necessary before installing solar power plants. This study compared ground-based measurements with satellite-based data and found significant differences. Real-time measurements are essential for accurately assessing solar energy resources.
Article
Mathematics, Interdisciplinary Applications
Yaoyao He, Yun Wang, Shuo Wang, Xin Yao
Summary: This article develops a wind speed probabilistic forecasting model based on the complete ensemble empirical mode decomposition, adaptive noise-least absolute shrinkage and selection operator, and quantile regression neural network, achieving high accuracy and robustness through multi-step prediction and probability density function fitting.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Environmental Sciences
Hee-Wook Choi, Yeon-Hee Kim, Keunhee Han, Chansoo Kim
Summary: The study utilized an EMOS model to assess probabilistic forecasts of low-level wind shear, finding that the model demonstrated better predictive skills compared to raw ensemble forecasts.
Article
Meteorology & Atmospheric Sciences
Maria Lakatos, Sebastian Lerch, Stephan Hemri, Sandor Baran
Summary: Ensemble forecasts are an important step in weather forecasting as they account for uncertainties in future atmospheric conditions. However, they often lack accuracy and require post-processing. Multivariate post-processing aims to capture spatial and temporal correlations among different dimensions to improve forecast accuracy. In this study, different non-parametric multivariate approaches are compared, and results show that multivariate post-processing significantly enhances forecast skill compared to raw and independently calibrated forecasts.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2023)
Article
Chemistry, Multidisciplinary
M. I. Dieste-Velasco, S. Garcia-Rodriguez, A. Garcia-Rodriguez, M. Diez-Mediavilla, C. Alonso-Tristan
Summary: This study proposes different models constructed with meteorological variables to determine horizontal ultraviolet irradiance (I-UV) based on data collected in Burgos, Spain from March 2020 to May 2022. The effectiveness of these models is explored by comparing supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was conducted to determine the variables for the models. The analysis of various variables reveals that the ANN models yield more accurate results than the regression models.
APPLIED SCIENCES-BASEL
(2023)
Article
Meteorology & Atmospheric Sciences
Yuanyuan Liu
Summary: This article discusses the use of the Kalman filter to improve solar irradiance predictions, and proposes ensemble prediction and measurement to enhance accuracy. Ensemble prediction also provides an estimation of solar irradiance uncertainty, which is useful for energy production forecasts.
ADVANCES IN METEOROLOGY
(2022)
Article
History & Philosophy Of Science
Corey Dethier
Summary: The application of statistics to ensemble-generated data in climate science can be challenging, and the reliability of the results should be evaluated on a case-by-case basis.
Article
Thermodynamics
Hao Zhang, Yong Shuai, Bachirou Guene Lougou, Boshu Jiang, Dazhi Yang, Qinghui Pan, Fuqiang Wang, Xing Huang
Summary: This study establishes a numerical model to find the optimal structural parameters of ceramic foam for solar thermochemistry applications. The results show that using ceramic foam with high porosity and large cell size can attain the best thermochemical characteristics.
Article
Green & Sustainable Science & Technology
Dazhi Yang, Gokhan Mert Yagli, Dipti Srinivasan
Summary: The paper introduces a state-of-the-art probabilistic solar forecasting method that effectively addresses the challenges of reflecting high-frequency fluctuations and changing uncertainty in solar energy systems, benefiting real-time stochastic simulations on a large scale.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Engineering, Electrical & Electronic
Nantian Huang, Qingkui He, Jiajin Qi, Qiankun Hu, Rijun Wang, Guowei Cai, Dazhi Yang
Summary: A method for forecasting the spatial-temporal distribution of electric vehicle charging load in a multi-node charging scenario is proposed. The method considers the spatial correlation between nodes and utilizes a generative adversarial network to characterize the randomness of the charging load. Simulation results demonstrate that the method outperforms state-of-the-art models in terms of interval prediction accuracy and coverage.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Disong Fu, Christian A. Gueymard, Dazhi Yang, Yu Zheng, Xiangao Xia, Jianchun Bian
Summary: The latest version of Level-3 AHI AOD product underestimates aerosol optical depth against ground measurements. An XGBoost model based on AHI AOD, meteorological quantities, and geographic information has been developed to correct these errors, resulting in a significant improvement in the corrected AOD values.
ATMOSPHERIC RESEARCH
(2023)
Article
Thermodynamics
Hao Zhang, Xiaomi Zhang, Dazhi Yang, Yong Shuai, Bachirou Guene Lougou, Qinghui Pan, Fuqiang Wang
Summary: This study compares the thermochemical reaction characteristics of six common ferrites and four metal dopants through thermogravimetric analysis. A modified oxygen carrier with low reaction temperature requirements and excellent reaction performance is found, achieving dual optimization in terms of thermal efficiency and chemical efficiency. A foam-structured material with SiC as support is prepared and experimentally tested, showing optimal reaction performance with the highest CO yield of 439 mu mol/g and a peak CO yield of 7.0 mL min-1 g-1 and CO2 conversion of 45.5% at a reduction temperature of only 1100 degrees C.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Engineering, Electrical & Electronic
Gang Zhang, Xiao Chen, Dazhi Yang, Alistair Duffy, Ming Li, Lixin Wang
Summary: This article investigates how crosstalk amplitudes of multiconductor polyvinyl chloride cables vary with heating temperatures and time, and proposes a method to estimate aging rates and accelerating ratios.
IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY
(2023)
Article
Automation & Control Systems
Xiaowei Ju, Yuan Cheng, Bochao Du, Mingliang Yang, Dazhi Yang, Shumei Cui
Summary: To address the issue of high-frequency ac loss in hairpin windings, a hybrid transposed hairpin winding (HTHW) scheme is proposed, combining flat wire and litz wire. The HTHW can significantly reduce ac loss while maintaining a high slot fill factor.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Materials Science, Characterization & Testing
Qi Li, Hai Zhang, Jue Hu, Stefano Sfarra, Miranda Mostacci, Dazhi Yang, Marc Georges, Vladimir P. Vavilov, Xavier P. V. Maldague
Summary: With increasing attention paid to the protection of cultural relics, non-destructive testing (NDT) technologies such as infrared thermography (IRT) and Terahertz time-domain spectroscopy (THz-TDS) are proving to be valuable in detecting defects in ancient buildings and artworks. The present study explores the integration of online/offline background segmentation algorithms, commonly used in video processing, as a feature extraction tool with NDT. Experimental results demonstrate the superior performance of the proposed novel algorithms in detecting defects in a replica painting.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2023)
Article
Green & Sustainable Science & Technology
Martin Janos Mayer, Dazhi Yang
Summary: This study investigates the uncertainty in photovoltaic (PV) power forecasting by using ensemble numerical weather prediction (NWP) and ensemble model chain methods. It is demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, the point forecast accuracy is significantly improved through this pairing strategy. The recommended strategy achieves a mean-normalized continuous ranked probability score of 18.4% and a root mean square error of 42.1%.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Computer Science, Information Systems
Gang Zhang, Xiao Chen, Dazhi Yang, Lixin Wang, Xin He, Zhehao Zhang
Summary: This paper investigates the phase stability of a phase stable cable using multi-physics coupling simulations. A three-dimensional electromagnetic-thermal-flow-mechanics multi-physics coupling model is established to simulate the cable's behavior in air. The results reveal differences in the electric field distribution and thermal deformation between the corrugated cable and a normal coaxial cable, highlighting the need for higher voltage endurance and design optimization.
Article
Thermodynamics
Dongxu Shen, Chao Lyu, Dazhi Yang, Gareth Hinds, Lixin Wang
Summary: This work proposes a novel connection fault diagnosis method based on mechanical vibration signals rather than voltage and current measurements. The simulation of the vibration environment and optimal sensor placement are achieved, and a broad belief network (BBN) is proposed for detecting and locating connection faults in lithium-ion battery packs based on the vibration signals. Incremental-learning algorithms are paired with the BBN to adapt to new data in real-time. The empirical evidence shows a diagnostic accuracy of 93.25%, demonstrating the effectiveness and feasibility of the proposed method.
Article
Thermodynamics
Guoming Yang, Hao Zhang, Wenting Wang, Bai Liu, Chao Lyu, Dazhi Yang
Summary: This study presents a capacity optimization model and economic analysis for PV-hydrogen hybrid systems, using physical modeling of PV to enhance profit. It also identifies government subsidy policy, prices and costs, and PV power utilization as influential factors for system optimization.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Bai Liu, Dazhi Yang, Martin Janos Mayer, Carlos F. M. Coimbra, Jan Kleissl, Merlinde Kay, Wenting Wang, Jamie M. Bright, Xiang'ao Xia, Xin Lv, Dipti Srinivasan, Yan Wu, Hans Georg Beyerj, Gokhan Mert Yagli, Yanbo Shenl
Summary: Current solar forecast verification processes mainly focus on performance comparison of competing methods. However, it is important to evaluate the best method relative to the best-possible performance under specific forecasting situations, and quantify predictability and forecast skill. Unfortunately, there is a lack of literature on the quantification of relative performance of solar irradiance, and few studies on the spatial distributions of predictability and forecast skill. This study quantifies and maps the predictability and forecast skill of solar irradiance in the United States, refutes misconceptions, and revives the formulation of skill score.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Electrochemistry
Miao Bai, Chao Lyu, Dazhi Yang, Gareth Hinds
Summary: Accurate evaluation of the health status of lithium-ion batteries is crucial for their utility and safety. Electrochemical impedance spectroscopy (EIS) has advantages in detecting lithium plating, but its ability to quantify the degree of lithium plating has not been fully explored. This study proposes an EIS-based method that uses impedance spectrum to estimate battery capacity loss and quantify the mass of lithium plating.
Article
Engineering, Electrical & Electronic
Gang Zhang, Xin He, Lixin Wang, Dazhi Yang, Kaixing Chang, Alistair Duffy
Summary: Soft faults in cables can cause short circuits and open circuits, which need to be detected and eliminated as early as possible for the safe and stable operation of the cables. The time-reversal multiple signal classification (TR-MUSIC) method has been proven effective for locating soft faults in cables due to its high resolution and noise robustness. However, traditional TR-MUSIC requires a vector network analyzer (VNA) for measuring the scattering matrix of cables, which adds complexity and cost. To address this, a new method is proposed using an arbitrary function generator and an oscilloscope to acquire the desired scattering parameters.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Energy & Fuels
Siddharth Sradhasagar, Omkar Subhasish Khuntia, Srikanta Biswal, Sougat Purohit, Amritendu Roy
Summary: In this study, machine learning models were developed to predict the bandgap and its character of double perovskite materials, with LGBMRegressor and XGBClassifier models identified as the best predictors. These models were further employed to predict the bandgap of novel bismuth-based transition metal oxide double perovskites, showing high accuracy, especially in the range of 1.2-1.8 eV.
Article
Energy & Fuels
Wei Shuai, Haoran Xu, Baoyang Luo, Yihui Huang, Dong Chen, Peiwang Zhu, Gang Xiao
Summary: In this study, a hybrid model based on numerical simulation and deep learning is proposed for the optimization and operation of solar receivers. By applying the model to different application scenarios and considering multiple performance objectives, small errors are achieved and optimal structure parameters and heliostat scales are identified. This approach is not only applicable to gas turbines but also heating systems.
Article
Energy & Fuels
Mubashar Ali, Zunaira Bibi, M. W. Younis, Muhammad Mubashir, Muqaddas Iqbal, Muhammad Usman Ali, Muhammad Asif Iqbal
Summary: This study investigates the structural, mechanical, and optoelectronic properties of the BaCuF3 fluoroperovskite using the first-principles modelling approach. The stability and characteristics of different cubic structures of BaCuF3 are evaluated, and the alpha-BaCuF3 and beta-BaCuF3 compounds are found to be mechanically stable with favorable optical properties for solar cells and high-frequency UV applications.
Article
Energy & Fuels
Dong Le Khac, Shahariar Chowdhury, Asmaa Soheil Najm, Montri Luengchavanon, Araa mebdir Holi, Mohammad Shah Jamal, Chin Hua Chia, Kuaanan Techato, Vidhya Selvanathan
Summary: A novel recycling system is proposed in this study to decompose and reclaim the constituent materials of organic-inorganic perovskite solar cells (PSCs). By utilizing a one-step solution process extraction approach, the chemical composition of each layer is successfully preserved, enabling their potential reuse. The proposed recycling technique helps mitigate pollution risks, minimize waste generation, and reduce recycling costs.
Article
Energy & Fuels
Peijie Lin, Feng Guo, Xiaoyang Lu, Qianying Zheng, Shuying Cheng, Yaohai Lin, Zhicong Chen, Lijun Wu, Zhuang Qian
Summary: This paper proposes an open-set fault diagnosis model for PV arrays based on 1D VoVNet-SVDD. The model accurately diagnoses various types of faults and is capable of identifying unknown fault types.