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
Engineering, Environmental
G. Dagher, A. Martin, J. M. Galharret, L. Moulin, J. P. Croue, B. Teychene
Summary: This study proposes the application of time series analysis and exponential smoothing (ETS) to accurately predict multicycle membrane fouling. The ETS models showed great interpretability and were found to be effective in both lab-scale filtration tests and natural water resources filtration. Additionally, the ETS models were applied to predict the permeability variation of drinking water treatment plant membrane skid, yielding satisfactory results. A web application was also developed to assist membrane users in utilizing ETS models.
JOURNAL OF WATER PROCESS ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Erol Egrioglu, Eren Bas
Summary: A new hybrid recurrent artificial neural network is proposed for nonlinear time series forecasting in this study. The network combines simple exponential smoothing and a single multiplicative neuron model to solve the insufficiency of classical forecasting methods in forecasting nonlinear and complex time series structures. The proposed method outperforms other artificial neural networks in terms of performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Economics
Fotios Petropoulos, Spyros Makridakis, Neophytos Stylianou
Summary: Forecasting the outcome of outbreaks is crucial for decision-making, but the severity and the socioeconomic consequences of outbreaks are difficult to predict. This paper presents a statistical time series approach to model and predict the short-term behavior of COVID-19, which offers competitive forecast accuracy and estimates of uncertainty.
INTERNATIONAL JOURNAL OF FORECASTING
(2022)
Article
Mathematics, Applied
Babak Emami
Summary: This approach proposes a differential geometry-based method for time series forecasting by treating time series data as paths on a manifold, and computing the manifold connection to forecast the variables.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Energy & Fuels
Shreya Sajid, Surender Reddy Salkuti, C. Praneetha, K. Nisha
Summary: This paper focuses on short-term wind speed forecasting using time series methods. Various time series forecasting techniques are applied and compared using performance metrics. A novel LSTM-ARIMA model is proposed, which achieves the highest prediction accuracy and the least error metrics at all time scales.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2022)
Article
Economics
Erick Meira, Fernando Luiz Cyrino Oliveira, Jooyoung Jeon
Summary: The study introduces a new method of model selection to enhance the predictive power of automated exponential smoothing routines using prediction intervals, along with a pruning strategy to improve accuracy. Empirical experiments show that these simple methods substantially improve forecasting accuracy.
INTERNATIONAL JOURNAL OF FORECASTING
(2021)
Article
Computer Science, Theory & Methods
Aji Prasetya Wibawa, Agung Bella Putra Utama, Hakkun Elmunsyah, Utomo Pujianto, Felix Andika Dwiyanto, Leonel Hernandez
Summary: This study introduces a novel hybrid approach called Smoothed-CNN (S-CNN) that combines exponential smoothing with CNN, outperforming other forecasting methods such as MLP and LSTM. The results show that S-CNN is better than MLP and LSTM, achieving the best MSE of 0.012147693 with 76 hidden layers at an 80%:20% data composition.
JOURNAL OF BIG DATA
(2022)
Article
Physics, Multidisciplinary
Yuliya Shapovalova, Nalan Basturk, Michael Eichler
Summary: This paper reviews two models for count data, one based on observation and the other based on parameters, and compares their forecasting performance on simulated and real datasets. The findings show that both models have advantages in different situations, and discuss the pros and cons of inference for both models in detail.
Article
Computer Science, Artificial Intelligence
Ioannis E. Livieris, Stavros Stavroyiannis, Lazaros Iliadis, Panagiotis Pintelas
Summary: This study introduces a new framework for enhancing deep learning time-series models based on data preprocessing techniques. The framework focuses on generating high-quality time-series data through a series of transformations, significantly improving forecasting performance as demonstrated in comprehensive numerical experiments and statistical analysis.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Souad Larabi-Marie-Sainte, Sawsan Alhalawani, Sara Shaheen, Khaled Mohamad Almustafa, Tanzila Saba, Fatima Nayer Khan, Amjad Rehman
Summary: This study successfully forecasted the number of COVID19 cases and deaths using time-series and statistical forecasting techniques. By validating the models, it was found that the proposed ETS model showed superior performance in forecasting.
Article
Computer Science, Artificial Intelligence
Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso
Summary: This paper introduces a machine learning approach for forecasting hierarchical time series, using a deep neural network to directly generate accurate and reconciled forecasts, while incorporating explanatory variables to improve forecasting accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
Moiz Qureshi, Nawaz Ahmad, Saif Ullah, Ahmed Raza ul Mustafa
Summary: Forecasting is a popular topic in various disciplines due to the uncertainty of underlying phenomena, which can be estimated using mathematical functions. As technology advances, algorithms are updated to capture the ongoing phenomena. Machine learning algorithms, such as MLP, ELM, ARIMA, and ES models, are utilized to model and predict the real exchange rate data set. The study split the data into training and testing, and the model that best meets the KPI criteria is selected for predicting the behavior of the real exchange rate data set.
Article
Environmental Sciences
Tugba Memisoglu Baykal, H. Ebru Colak, Cebrail Kilinc
Summary: Future-oriented forecasts play a crucial role in making forward-looking decisions and planning. This study used Geographic Information Systems (GIS) to produce predictive climate boundary maps for 13 selected provinces in Turkey, and made predictions about future climate changes using climate classification and time series methods. The study provides innovative analysis approaches that can contribute to various fields of research.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Computer Science, Information Systems
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: In this paper, a Bayesian optimization-based dynamic ensemble (BODE) method is proposed for time series forecasting. The BODE method combines ten different model candidates and uses a model-based Bayesian optimization algorithm for combination hyperparameter tuning. The method demonstrates robust performance and better generalization capability.
INFORMATION SCIENCES
(2022)
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
Energy & Fuels
Xixi Sun, Dazhi Yang, Christian A. Gueymard, Jamie M. Bright, Peng Wang
Summary: This study examines the impact of gridded data on the performance of clear-sky radiation models at the urban scale. Using Singapore as a case study, various models are compared with in situ irradiance measurements to assess their performance. The study finds that the spatially-averaged inputs cannot fully represent the micro-climatic variability, leading to significant differences in performance between models. There is no clear-sky radiation model that significantly outperforms its peers.
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
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.
