Article
Thermodynamics
Jikai Duan, Hongchao Zuo, Yulong Bai, Jizheng Duan, Mingheng Chang, Bolong Chen
Summary: This study presents a novel hybrid forecasting system that significantly enhances the accuracy of wind speed prediction, achieving superior prediction results compared to other models through a process of decomposition, prediction, and error correction.
Article
Computer Science, Artificial Intelligence
Sourav Kumar Purohit, Sibarama Panigrahi, Prabira Kumar Sethy, Santi Kumari Behera
Summary: Accurate prediction of crop prices is crucial for farmers and government. This study proposes hybrid methods to predict the prices of three commonly used vegetable crops in India, showing superiority over statistical models and machine learning models through extensive statistical analyses.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jianzhou Wang, Haipeng Zhang, Qiwei Li, Aini Ji
Summary: Wind energy forecasting and analysis are crucial for the management and operation of wind farms. In this study, a hybrid forecasting system including point forecasting, interval forecasting, and evaluation was designed to provide technical support for optimal coordinated grid dispatching and improve grid stability.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Thermodynamics
Fei Wang, Shuang Tong, Yiqian Sun, Yongsheng Xie, Zhao Zhen, Guoqing Li, Chunmei Cao, Neven Duic, Dagui Liu
Summary: This paper proposes an ultra-short-term wind speed hybrid prediction method based on wind process pattern forecasting. By dividing the wind process into different patterns and selecting the corresponding prediction model based on the pattern, the proposed method can reliably forecast future wind speeds.
Article
Thermodynamics
Srihari Parri, Kiran Teeparthi, Vishalteja Kosana
Summary: Wind energy is gaining attention globally, and accurately predicting wind speed is a challenge due to its unpredictable nature. This study introduces a novel method called VMD-CoST-SVR, which combines variational mode decomposition, contrastive learning of seasonal-trend representations, and support vector regression. The method denoises wind speed data, extracts trend and seasonal features, and predicts future wind speed. Experimental results show that the proposed approach achieves significant improvement across different time intervals.
Article
Computer Science, Information Systems
Paulo S. G. de Mattos Neto, Joao F. L. de Oliveira, Domingos S. de O. Santos Junior, Hugo Valadares Siqueira, Manoel H. N. Marinho, Francisco Madeiro
Summary: The integration of wind power into traditional electricity grids is challenging due to the unpredictable nature of wind speed. Wind speed forecasting is crucial for decision-making in the energy sector, and this paper proposes a method to improve its accuracy by combining linear and nonlinear forecasts with exogenous variables. Results show that this method outperforms statistical techniques, Machine Learning models, and hybrid systems in most cases.
INFORMATION SCIENCES
(2021)
Article
Green & Sustainable Science & Technology
Chen Wang, Shenghui Zhang, Peng Liao, Tonglin Fu
Summary: This paper proposes a novel multi-objective optimization algorithm to optimize the parameters of wind power models and utilizes a model selection strategy to select the optimal hybrid models for improved accuracy and stability in forecasting. The research findings demonstrate the feasibility of wind power conversion based on wind speed forecasting and show that the optimal model is more reliable and accurate compared to other models.
Article
Energy & Fuels
Anil Kumar Kushwah, Rajesh Wadhvani
Summary: The study proposed a hybrid approach using data complexity to determine the use of decomposition technique, performing well on complex time series and achieving significant improvement on stationary wind time series.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2021)
Article
Energy & Fuels
Yunsun Kim, Sahm Kim
Summary: This study compared methods to forecast the increase in power consumption due to the rise of electric vehicles, proposing excellent models for each region based on scaled geographical datasets over two years. The study found that EV charging volumes are influenced by various factors, but power suppliers struggle to access this information. By comparing modeling techniques and evaluating exogenous variables, the importance of historic data was confirmed in predicting charging demands in different geographical areas.
Article
Statistics & Probability
Dileep Kumar Shetty, B. Ismail
Summary: In this study, a hybrid non-stationary model based on Elman's Recurrent Neural Networks was developed to forecast stock market price indices. The model is capable of capturing both linear and non-linear structures, and it exhibited the best forecasting accuracy when applied to real datasets.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Energy & Fuels
Meftah Elsaraiti, Adel Merabet
Summary: This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), compared to the common autoregressive integrated moving average (ARIMA). The comparison result shows that the LSTM method is more accurate than ARIMA.
Article
Computer Science, Artificial Intelligence
Ranjit Kumar Paul, Sandip Garai
Summary: Accurate forecasting in Indian agriculture is crucial, with machine learning techniques like artificial neural network and wavelet transformation being effective in handling nonlinear datasets to improve model accuracy.
Article
Green & Sustainable Science & Technology
Alma Rosa Mendez-Gordillo, Rafael Campos-Amezcua, Erasmo Cadenas
Summary: This study proposes a hybrid model for wind speed forecasting, combining Multiplicative Cascade and Persistence techniques. The performance of the hybrid model is found to exceed that of the individual models, as evaluated using various error metrics.
Article
Automation & Control Systems
Sivanagaraja Tatinati, Yubo Wang, Andy W. H. Khong
Summary: This article proposes a hybrid method combining elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) to forecast Gaussian heteroscedastic wind speed time-series. The eVMD algorithm assesses the nonstationary characteristics of the wind speed signal and decomposes it into intrinsic components (ICs). The fRCN method uses local receptive fields to extract features related to local variations and global trends in each IC, which are then learned using extreme learning machines theories. An ensemble unit is utilized to determine appropriate weightages for each forecasted IC before producing the final forecasting values.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Geochemistry & Geophysics
Marek Brabec, Alexandra Craciun, Alexandru Dumitrescu
Summary: Wind speed forecast was improved by combining numerical weather prediction simulations and statistical models. The most important factors in the calibration model are wind speed observations from the past 24 hours and the simulated wind speed effect in relation to altitude.
JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Erasmo Cadenas, Wilfrido Rivera, Rafael Campos-Amezcua, Roberto Cadenas
NEURAL COMPUTING & APPLICATIONS
(2016)
Article
Thermodynamics
L. A. Dominguez-Inzunza, J. A. Hernandez-Magallanes, P. Soto, C. Jimenez, G. Gutierrez-Urueta, W. Rivera
APPLIED THERMAL ENGINEERING
(2016)
Article
Thermodynamics
C. L. Heard, W. Rivera, R. Best
APPLIED THERMAL ENGINEERING
(2016)
Article
Energy & Fuels
Erasmo Cadenas, Wilfrido Rivera, Rafael Campos-Amezcua, Christopher Heard
Article
Thermodynamics
J. Ibarra-Bahena, U. Dehesa-Carrasco, R. J. Romero, B. Rivas-Herrera, W. Rivera
EXPERIMENTAL THERMAL AND FLUID SCIENCE
(2017)
Article
Thermodynamics
G. Gutierrez Urueta, A. Huicochea, W. Rivera, P. Rodriguez-Aumente, Francisco Oviedo-Tolentino
INTERNATIONAL JOURNAL OF EXERGY
(2017)
Article
Thermodynamics
W. Rivera, A. Huicochea, R. J. Romero, A. Lozano
APPLIED THERMAL ENGINEERING
(2018)
Article
Thermodynamics
J. Ibarra-Bahena, W. Rivera, R. J. Romero, M. Montiel-Gonzalez, U. Dehesa-Carrasco
APPLIED THERMAL ENGINEERING
(2018)
Article
Energy & Fuels
C. L. Heard, W. Rivera, R. Best
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2018)
Article
Thermodynamics
J. A. Hernandez-Magallanes, C. L. Heard, R. Best, W. Rivera
Article
Energy & Fuels
J. Camilo Jimenez-Garcia, Wilfrido Rivera
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2018)
Article
Thermodynamics
J. Camilo Jimenez-Garcia, W. Rivera
APPLIED THERMAL ENGINEERING
(2019)
Article
Engineering, Chemical
Juliana Saucedo-Velazquez, Geydy Gutierrez-Urueta, Jorge Alejandro Wong-Loya, Ricardo Molina-Rodea, Wilfrido Rivera Gomez Franco
Summary: This paper analyzes a geothermal field in Mexico to compare the performance of different advanced absorption cooling systems driven by geothermal heat. The results show that utilizing the water temperature from an abandoned geothermal well can achieve high temperatures and different systems have varying maximum cooling potential.
Article
Thermodynamics
P. Soto, L. A. Dominguez-Inzunza, W. Rivera
CASE STUDIES IN THERMAL ENGINEERING
(2018)
Article
Thermodynamics
J. A. Hernandez-Magallanes, W. Rivera, A. Coronas
APPLIED THERMAL ENGINEERING
(2017)
Article
Green & Sustainable Science & Technology
Cameron Bracken, Nathalie Voisin, Casey D. Burleyson, Allison M. Campbell, Z. Jason Hou, Daniel Broman
Summary: This study presents a methodology and dataset for examining compound wind and solar energy droughts, as well as the first standardized benchmark of energy droughts across the Continental United States (CONUS) for a 2020 infrastructure. The results show that compound wind and solar droughts have distinct spatial and temporal patterns across the CONUS, and the characteristics of energy droughts are regional. The study also finds that compound high load events occur more often during compound wind and solar droughts than expected.
Article
Green & Sustainable Science & Technology
Ning Zhang, Yanghao Yu, Jiawei Wu, Ershun Du, Shuming Zhang, Jinyu Xiao
Summary: This paper provides insights into the optimal configuration of CSP plants with different penetrations of wind power by proposing an unconstrained optimization model. The results suggest that large solar multiples and TES are preferred in order to maximize profit, especially when combined with high penetrations of wind and photovoltaic plants. Additionally, the study demonstrates the economy and feasibility of installing electric heaters (EH) in CSP plants, which show a linear correlation with the penetration of variable energy resources.
Article
Green & Sustainable Science & Technology
M. Szubel, K. Papis-Fraczek, S. Podlasek
Article
Green & Sustainable Science & Technology
J. Silva, J. C. Goncalves, C. Rocha, J. Vilaca, L. M. Madeira
Summary: This study investigated the methanation of CO2 in biogas and compared two different methanation reactors. The results showed that the cooled reactor without CO2 separation achieved a CO2 conversion rate of 91.8%, while the adiabatic reactors achieved conversion rates of 59.6% and 67.2%, resulting in an overall conversion rate of 93.0%. Economic analysis revealed negative net present worth values, indicating the need for government monetary incentives.
Article
Green & Sustainable Science & Technology
Yang Liu, Yonglan Xi, Xiaomei Ye, Yingpeng Zhang, Chengcheng Wang, Zhaoyan Jia, Chunhui Cao, Ting Han, Jing Du, Xiangping Kong, Zhongbing Chen
Summary: This study investigated the effect of using nanofiber membrane composites containing Prussian blue-like compound nanoparticles (PNPs) to relieve ammonia nitrogen inhibition of rural organic household waste during high-solid anaerobic digestion and increase methane production. The results showed that adding NMCs with 15% PNPs can lower the concentrations of volatile fatty acids and ammonia nitrogen, and increase methane yield.
Article
Green & Sustainable Science & Technology
Zhong Ge, Xiaodong Wang, Jian Li, Jian Xu, Jianbin Xie, Zhiyong Xie, Ruiqu Ma
Summary: This study evaluates the thermodynamic, exergy, and economic performance of a double-stage organic flash cycle (DOFC) using ten eco-friendly hydrofluoroolefins. The influences of key parameters on performance are analyzed, and the advantages of DOFC over single-stage type are quantified.
Article
Green & Sustainable Science & Technology
Nicolas Kirchner-Bossi, Fernando Porte-Agel
Summary: This study investigates the optimization of power density in wind farms and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced to optimize power density and turbine layout. The results show that the PDGA-driven solutions significantly reduce the levelized cost of energy (LCOE) compared to the default layout, and exhibit a convex relationship between area and LCOE or power density.
Article
Green & Sustainable Science & Technology
Chunxiao Zhang, Dongdong Li, Lin Wang, Qingpo Yang, Yutao Guo, Wei Zhang, Chao Shen, Jihong Pu
Summary: In this study, a novel reversible liquid-filled energy-saving window that effectively regulates indoor solar radiation heat gain is proposed. Experimental results show that this window can effectively reduce indoor temperature during both summer and winter seasons, while having minimal impact on indoor illuminance.
Article
Green & Sustainable Science & Technology
Alessandro L. Aguiar, Martinho Marta-Almeida, Mauro Cirano, Janini Pereira, Leticia Cotrim da Cunha
Summary: This study analyzed the Brazilian Equatorial Shelf using a high-resolution ocean model and found significant tidal variations in the area. Several hypothetical barrages were proposed with higher annual power generation than existing barrages. The study also evaluated the installation effort of these barrages.
Article
Green & Sustainable Science & Technology
Francesco Superchi, Nathan Giovannini, Antonis Moustakis, George Pechlivanoglou, Alessandro Bianchini
Summary: This study focuses on the optimization of a hybrid power station on the Tilos island in Greece, aiming to increase energy export and revenue by optimizing energy fluxes. Different scenarios are proposed to examine the impact of different agreements with the grid operator on the optimal solution.
Article
Green & Sustainable Science & Technology
Peimaneh Shirazi, Amirmohammad Behzadi, Pouria Ahmadi, Sasan Sadrizadeh
Summary: This research presents two novel energy production/storage/usage systems to reduce energy consumption and environmental effects in buildings. A biomass-fired model and a solar-driven system integrated with photovoltaic thermal (PVT) panels and a heat pump were designed and assessed. The results indicate that the solar-based system has an acceptable energy cost and the PVT-based system with a heat pump is environmentally superior. The biomass-fired system shows excellent efficiency.
Article
Green & Sustainable Science & Technology
Zihao Qi, Yingling Cai, Yunxiang Cui
Summary: This study aims to investigate the operational characteristics of the solar-ground source heat pump system (SGSHPS) in Shanghai under different operation modes. It concludes that tandem operation mode 1 is the optimal mode for winter operation in terms of energy efficiency.
Article
Green & Sustainable Science & Technology
L. Bartolucci, S. Cordiner, A. Di Carlo, A. Gallifuoco, P. Mele, V. Mulone
Summary: Spent coffee grounds are a valuable biogenic waste that can be used as a source of biofuels and valuable chemicals through pyrolysis and solvent extraction processes. The study found that heavy organic bio-oil derived from coffee grounds can be used as a carbon-rich biofuel, while solvent extraction can extract xantines and p-benzoquinone, which are important chemicals for various industries. The results highlight the promising potential of solvent extraction in improving the economic viability of coffee grounds pyrolysis-based biorefineries.
Article
Green & Sustainable Science & Technology
Luiza de Queiroz Correa, Diego Bagnis, Pedro Rabelo Melo Franco, Esly Ferreira da Costa Junior, Andrea Oliveira Souza da Costa
Summary: Building-integrated photovoltaics, especially organic solar technology, are important for reducing greenhouse gas emissions in the building sector. This study analyzed the performance of organic panels laminated in glass in a vertical installation in Latin America. Results showed that glass lamination and vertical orientation preserved the panels' performance and led to higher energy generation in winter.
Article
Green & Sustainable Science & Technology
Zhipei Hu, Shuo Jiang, Zhigao Sun, Jun Li
Summary: This study proposes innovative fin arrangements to enhance the thermal performance of latent heat storage units. Through optimization of fin distribution and prediction of transient melting behaviors, it is found that fin structures significantly influence heat transfer characteristics and melting behaviors.