Article
Computer Science, Artificial Intelligence
Simon Rudkin, Wanling Qiu, Pawel Dlotko
Summary: Norms of Persistent Homology, introduced in topological data analysis, serve as indicators of system instability and resemble financial market uncertainty indexes. This paper establishes a contemporaneous relationship between persistence norms and all types of uncertainty. By incorporating a dynamic modeling framework, it is demonstrated that unobserved uncertainty causes persistence norms. Furthermore, the study reveals that correlations between unobserved uncertainty and persistence norms increase before market crashes. The evidence from the dynamic analysis supports the conclusion that persistence norms can signal impending market crashes.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Alessandra Cipriani, Christian Hirsch, Martina Vittorietti
Summary: In many fields, such as materials science, 3D information is often obtained by extrapolating from 2D slices. Persistence vineyards have emerged as a powerful tool in topological data analysis to consider topological features across multiple slices. This article demonstrates how persistence vineyards can be used to design statistical hypothesis tests for 3D microstructure models based on 2D slice data. The testing methodology is illustrated through simulations and an example from materials science.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Statistics & Probability
Sanjay Kumar, Nand Kumar
Summary: This paper examines the time series of the Exchange Market Pressure Index (EMPI) for eleven countries and tests the normality of the data using various statistical tests. It also introduces a new test for serially correlated data and compares the power of different tests. The findings indicate that the EMPI time series is non-normal, with two tests being the most powerful.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Physics, Fluids & Plasmas
Jordi Baro, Mehdi Pouragha, Richard Wan, Jorn Davidsen
Summary: Granular media under quasistatic loading can reach a stable evolution state characterized by sudden transitions and scale-free avalanches. The statistical properties and susceptibilities of the system are nonuniversal, depending on interactions between particles, and exhibit lower scaling exponents for softer interactions, similar to observations in amorphous solids at their critical point. The relationship between microscopic and macroscopic variables, including external stress drops and internal potential energy release during kinetic avalanches, is discussed.
Article
Operations Research & Management Science
Fredj Jawadi, Abdoul karim Idi Cheffou, Nabila Jawadi
Summary: This study reexamines the relationship between oil prices and Islamic finance over the past two decades for developed and emerging countries using a multicriteria time series approach. The findings suggest that while the oil sector significantly influences Islamic stock indices, there is a lack of evidence supporting a long-term relationship and mean reversion between these two markets, particularly in developed countries. However, since the COVID-19 pandemic, a mean reversion in the Islamic stock market has occurred, indicating the presence of a cointegration relationship and an active adjustment mechanism driven by investor behavior and market anxiety. These findings indicate integration of oil price and Islamic finance and market inefficiency.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Economics
Byunghoon Kang
Summary: This paper introduces a method for consistent inference of conditional mean functions in nonparametric series estimations that maintains consistency in the number of series terms. The method includes constructing confidence intervals and bands, as well as inference methods for the parametric part.
ECONOMETRIC THEORY
(2021)
Article
Economics
Tri M. Hoang
Summary: By employing both the mean-variance framework and common portfolio risk-optimization methods, this study examines the optimal weights for emerging and frontier markets. It finds that the market timing portfolio performs well, providing stable and positive returns. Additionally, volatility forecasts can be successfully converted into higher portfolio returns using quantitative investment approaches.
COGENT ECONOMICS & FINANCE
(2022)
Article
Critical Care Medicine
Ismail Tuna Geldigitti, Burcin Halacli, Berrin Er, Mehmet Yildirim, Gulay Tok, Ebru Ortac Ersoy, Serpil Ocal, Arzu Topeli
Summary: The study aimed to evaluate the persistence of abnormalities in laboratory variables regarding inflammation and coagulation during the recovery process of critically-ill COVID-19 patients. It was found that abnormality of laboratory tests persisted after hospital discharge and even after 90 days after ICU admission, suggesting a sustained risk for multi-organ damage and thromboembolic events in survivors.
JOURNAL OF CRITICAL & INTENSIVE CARE
(2022)
Article
Green & Sustainable Science & Technology
G. Notton, J. L. Duchaud, M. L. Nivet, C. Voyant, K. Chalvatzis, A. Fouilloy
Article
Energy & Fuels
Dazhi Yang, Stefano Alessandrini, Javier Antonanzas, Fernando Antonanzas-Torres, Viorel Badescu, Hans Georg Beyer, Robert Blaga, John Boland, Jamie M. Bright, Carlos F. M. Coimbra, Mathieu David, Azeddine Frimane, Christian A. Gueymard, Tao Hong, Merlinde J. Kay, Sven Killinger, Jan Kleissl, Philippe Lauret, Elke Lorenz, Dennis van der Meer, Marius Paulescu, Richard Perez, Oscar Perpinan-Lamigueiro, Ian Marius Peters, Gordon Reikard, David Renne, Yves-Marie Saint-Drenan, Yong Shuai, Ruben Urraca, Hadrien Verbois, Frank Vignola, Cyril Voyant, Jie Zhang
Article
Energy & Fuels
Jean-Laurent Duchaud, Cyril Voyant, Alexis Fouilloy, Gilles Notton, Marie-Laure Nivet
Article
Green & Sustainable Science & Technology
Cyril Voyant, Philippe Lauret, Gilles Notton, Jean-Laurent Duchaud, Alexis Fouilloy, Mathieu David, Zaher Mundher Yaseen, Ted Soubdhan
Summary: A new methodology has been developed for estimating forecastability (F) by using the Monte Carlo method to estimate F from RMSE and the persistence predictor. The study found that F varies at different locations and is linked to errors obtained by machine learning prediction methods. The methodology was validated for three parameters affecting F estimation.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2021)
Article
Energy & Fuels
Mathieu David, John Boland, Luigi Cirocco, Philippe Lauret, Cyril Voyant
Summary: This study compares the economic value of different operational solar forecasts for a specific application, finding a linear relationship between error metrics and economic gains. An improvement of 1% point in mean absolute error (MAE) results in approximately a 2% increase in economic gain for a large-scale PV farm with Li-ion batteries in the Australian energy market context.
Article
Green & Sustainable Science & Technology
Kacem Gairaa, Cyril Voyant, Gilles Notton, Said Benkaciali, Mawloud Guermoui
Summary: This paper investigates the prediction of solar energy by using multiple linear regression and artificial neural network models. It compares the performance of different combinations of inputs on two sites in Algeria. The study shows that adding ordinal variables to endogenous data can improve the accuracy of the prediction and simplify the implementation.
Article
Energy & Fuels
Hai Tao, Ahmed A. Ewees, Ali Omran Al-Sulttani, Ufuk Beyaztas, Mohammed Majeed Hameed, Sinan Q. Salih, Asaad M. Armanuos, Nadhir Al-Ansari, Cyril Voyant, Shamsuddin Shahid, Zaher Mundher Yaseen
Summary: The study introduced a novel intelligence model by hybridizing ANFIS with two metaheuristic optimization algorithms for accurate global solar radiation prediction. The proposed model outperformed other models by 25.7%-54.8% in terms of accuracy, showing potential for improvement in prediction accuracy through hybridization.
Article
Green & Sustainable Science & Technology
Cyril Voyant, Gilles Notton, Jean-Laurent Duchaud, Luis Antonio Garcia Gutierrez, Jamie M. Bright, Dazhi Yang
Summary: With the increasing share of intermittent renewable energy, advanced solar power forecasting models are needed to optimize the operation of solar power plants. This study compares the performance of advanced models with naive reference methods and considers the benefits of ensemble forecasting. The combination method and ARTU method statistically offer the best results for the proposed study conditions.
Article
Energy & Fuels
Dazhi Yang, Wenting Wang, Jamie M. Bright, Cyril Voyant, Gilles Notton, Gang Zhang, Chao Lyu
Summary: Forecasting global horizontal irradiance up to 12 hours ahead is crucial for solar photovoltaics grid integration. In this study, the ECMWF's HRES model and two NOAA models, namely RAP and HRRR, are validated and compared. Results show that HRES forecasts outperform HRRR and RAP forecasts in terms of accuracy.
Article
Green & Sustainable Science & Technology
S. Ouedraogo, G. A. Faggianelli, G. Notton, J. L. Duchaud, C. Voyant
Summary: This study investigates the influence of electricity price profiles on energy exchange planning in microgrids and explores energy management strategies and their impact on microgrid operation improvement. The study finds that electricity price profiles affect energy flow distribution and financial gains, while also observing that the battery size and limitation of power exchange with the main grid have different effects depending on the implemented energy management strategy.
Article
Chemistry, Multidisciplinary
Luis Garcia-Gutierrez, Cyril Voyant, Gilles Notton, Javier Almorox
Summary: This study introduces an innovative clustering method for solar radiation stations, utilizing both static and dynamic parameters for easier solar resource forecasting. The research found that only using mean and two dynamic parameters is sufficient to characterize solar irradiation behavior at each site, and recommends using k-means or hierarchical clustering for solar radiation clustering.
APPLIED SCIENCES-BASEL
(2022)
Article
Green & Sustainable Science & Technology
Cyril Voyant, Philippe Lauret, Gilles Notton, Jean-Laurent Duchaud, Luis Garcia-Gutierrez, Ghjuvan Antone Faggianelli
Summary: A new method for short-term probabilistic forecasting of global solar irradiance using complex-valued time series is explored. By using a complex autoregressive model, this approach generates deterministic and probabilistic forecasts that are in agreement with experimental data, sometimes exhibiting better accuracy than classical models.
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2022)
Article
Green & Sustainable Science & Technology
Jean-Laurent Duchaud, Ghjuvan-Antone Faggianelli, Cyril Voyant, Gilles Notton
Summary: Renewable energy micro-grids coupled with energy storage systems can be controlled using the Model Predictive Control (MPC) strategy. This paper focuses on how the time-step, horizon, and refresh period affect the optimal solution. Results show that using a time-step of 30 minutes and a 12-hour horizon yields good performances.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Review
Energy & Fuels
Cyril Voyant, Gilles Notton, Jean-Laurent Duchaud, Javier Almorox, Zaher Mundher Yaseen
RENEWABLE ENERGY FOCUS
(2020)
Article
Computer Science, Information Systems
Hai Tao, Sinan Q. Salih, Mandeep Kaur Saggi, Esmaeel Dodangeh, Cyril Voyant, Nadhir Al-Ansari, Zaher Mundher Yaseen, Shamsuddin Shahid
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.
