Article
Green & Sustainable Science & Technology
Haixiang Zang, Xin Jiang, LiLin Cheng, Fengchun Zhang, Zhinong Wei, Guoqiang Sun
Summary: This study proposes a modeling method for estimating solar radiation specifically for sites that cannot afford to install solar radiation measurement equipment. The method analyzes the correlation between meteorological factors and solar radiation, as well as the correlation of adjacent sites, and introduces a hybrid model to estimate solar radiation. The results of the study show that the proposed hybrid model outperforms benchmark models, and the method can be applied to different regions without solar radiation measurement.
Article
Green & Sustainable Science & Technology
Dongyu Jia, Liwei Yang, Tao Lv, Weiping Liu, Xiaoqing Gao, Jiaxin Zhou
Summary: This study assessed three machine learning models for predicting global and diffuse solar radiation in eight Chinese cities. The results showed that coastal locations had higher prediction error values compared to inland locations. The SVM model outperformed the other models in all locations, followed by GLMNET and RF. The accuracy of solar radiation prediction was closely related to weather and pollution conditions.
Article
Green & Sustainable Science & Technology
Shuting Zhao, Lifeng Wu, Youzhen Xiang, Jianhua Dong, Zhen Li, Xiaoqiang Liu, Zijun Tang, Han Wang, Xin Wang, Jiaqi An, Fucang Zhang, Zhijun Li
Summary: Simulation of solar radiation is highly important for sustainable development in the fields of energy, engineering, and more. The Himawari series of satellites, with their high temporal and spatial resolution, help overcome the issue of insufficient ground radiation observation in China. However, there is a need for improved accuracy in this data. By employing four machine learning models with 13 combinations of ground and satellite-based inputs, the simulation accuracy was significantly enhanced compared to using single-source data. The SVM13 model showed the best performance and higher simulation accuracy could be achieved by using a complex combination of meteorological factors as inputs.
Review
Thermodynamics
Javier Almorox, Cyril Voyant, Nadjem Bailek, Alban Kuriqi, J. A. Arnaldo
Summary: This study reviewed Total Solar Irradiance (TSI) and Solar Constant (SC), reevaluated them, and developed new seasonal radiation coefficient sets. It recommended using a TSI value of 1361 W m(-2) in solar radiation models for accuracy, highlighting the importance of local and seasonal calibration coefficients.
Article
Environmental Sciences
Brahim Belmahdi, Mohamed Louzazni, Abdelmajid El Bouardi
Summary: The paper proposes an algorithm to choose the optimal machine learning techniques and time series models to minimize forecasting errors, with a specific focus on solar radiation forecasting. The results indicated that the Feedforward neural network (FFNN) and ARIMA performed better and provided good approximations of the GSR output.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Olanrewaju M. Oyewola, Tchilabalo E. Patchali, Olusegun O. Ajide, Satyanand Singh, Olaniran J. Matthew
Summary: This study evaluates the performance of twenty empirical models in predicting Global Solar Radiation (GSR) in six meteorological stations within Fiji Islands. The results suggest that including air temperature and humidity as additional predictors, alongside with sunshine duration, day length, and extra-terrestrial radiation, produces the best GSR prediction over Fiji Islands.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
Zineb Bounoua, Laila Ouazzani Chahidi, Abdellah Mechaqrane
Summary: This study tested multiple models and techniques and found that the Random Forest method performed the best in estimating solar radiation accuracy. In the study of five locations in Morocco, the Temperature - Geographic factors model also proved to be a viable choice.
SUSTAINABLE MATERIALS AND TECHNOLOGIES
(2021)
Review
Thermodynamics
Yong Zhou, Yanfeng Liu, Dengjia Wang, Xiaojun Liu, Yingying Wang
Summary: This paper comprehensively reviews important aspects of machine-learning models in predicting global solar radiation, including input parameters and feature selection methods. It discusses different sources of input parameters and feature selection methods, and identifies seven classes of machine-learning models. Finally, it discusses the current and future research status.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Green & Sustainable Science & Technology
Shahid Husain, Uzair Ali Khan
Summary: The study found that machine learning models outperform empirical models for GSR prediction in different climate zones of India, though empirical models perform better in some areas. Temperature-based models, particularly k-nearest neighbors and XGBoost, are recommended for GSR prediction in regions with only air temperature data available. Combining future air temperature forecasts with KNN/XGBoost models can provide accurate GSR information for designing solar thermal systems in areas without solar radiation facilities.
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2022)
Article
Engineering, Multidisciplinary
Omer Ali Karaman, Tuba Tanyildizi Agir, Ismail Arsel
Summary: The study indicates that Extreme Learning Machines (ELM) outperform Artificial Neural Networks (ANN) in solar radiation estimation. Different activation functions were tested, with ELM showing better estimation performance. ELM achieved high accuracy with minimal error in a short amount of time, surpassing ANN.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Energy & Fuels
Olusola Bamisile, Ariyo Oluwasanmi, Chukwuebuka Ejiyi, Nasser Yimen, Sandra Obiora, Qi Huang
Summary: This study compares the predictive performance of machine learning and deep learning models for solar radiation prediction, showing that deep learning models have better accuracy in predicting GSR and DSR. However, machine learning models have shorter training and testing times, making them more suitable for low computational applications. Among all models developed in this study, the application of RNN for GSR prediction in Yobe had the best overall performance.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Fa Liu, Xunming Wang, Fubao Sun, Hong Wang
Summary: This study proposes models for accurate estimation of solar radiation and addresses issues in solar radiation observations in China. By combining meteorological data and machine learning models, the spatial and temporal coverage of solar radiation data is extended, and reliable solar radiation and PV power maps are created. The results show that high solar radiation and PV power are mainly distributed in northwestern China, Tibetan Plateau, and southern coastal areas. However, PV power has experienced a significant decline in the past decades.
Article
Ecology
Modeste Kameni Nematchoua, Jose A. Orosa, Marwa Afaifia
Summary: This study aims to predict the daily global solar radiation data of 27 cities in Europe using six different machine learning algorithms, and the SVM model was found to have the best performance according to the evaluation metrics.
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Arash Moradzadeh, Hamed Moayyed, Behnam Mohammadi-Ivatloo, Antonio Pedro Aguiar, Amjad Anvari-Moghaddam, Zulkurnain Abdul-Malek
Summary: This study proposes a global solar radiation forecasting approach based on federated learning and convolutional neural network, which accurately predicts solar irradiance and protects data privacy. The results show that this method performs well in solar radiation prediction in different regions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Hongrong Shi, Dazhi Yang, Wenting Wang, Disong Fu, Ling Gao, Jinqiang Zhang, Bo Hu, Yunpeng Shan, Yingjie Zhang, Yuxuan Bian, Hongbin Chen, Xiangao Xia
Summary: The latest Chinese geostationary meteorological satellite, FY-4A, provides precise measurements of solar reflection and thermal emission. This study utilizes FY-4A and a random forest model to estimate the global horizontal irradiance (GHI) over China, resulting in a solar photovoltaic (PV) resource map with high accuracy. The GHI retrieval shows better accuracy for lower solar zenith angles and larger errors in areas with bright surfaces and/or strong cloud transients. The annual mean PV resource map indicates significant regional variations, with the highest irradiance in Tibet and the poorest resource in the Sichuan Basin.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Computer Science, Information Systems
Boyu Qin, Wei Wang, Wei Li, Fan Li, Tao Ding
Summary: This article proposes a multi-objective energy management strategy for multiple pulsed power loads in shipboard integrated power systems (SIPSs). By identifying typical operation modes and incorporating utility and maneuverability into the comprehensive regulation objective, a system-level energy allocation scheme is formulated. The proposed method was validated through case studies.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Boyu Qin, Wansong Liu, Hengyi Li, Tao Ding, Ke Ma, Tianqi Liu
Summary: This paper studies the impact of inherent characteristics on the initial short-circuit current of MMC-based MTDC transmission systems and proposes a calculation method and quantitative indices to measure this impact. Through verification, it is found that the proposed method and indices can effectively evaluate the short-circuit current of the system and provide guidance for the planning, operation, and parameter selection of DC systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yuge Sun, Tao Ding, Ming Qu, Fengyu Wang, Mohammad Shahidehpour
Summary: A regional total transfer capability interval model based on the multi-dimensional holomorphic embedding method and sum of squares relaxation technique is proposed to solve the total transfer capability problem in high voltage direct current systems. The experimental results validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Wenhao Jia, Tao Ding, Jiawen Bai, Linquan Bai, Yongheng Yang, Frede Blaabjerg
Summary: This paper proposes a hybrid swapped battery charging and logistics dispatch model to optimize the combined operation of Battery Charging and Swapping Systems (BCSSs) for electric vehicles. By formulating the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem, the paper successfully combines the two models to address the challenges of long charging times and insufficient infrastructure for EVs.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Lilin Cheng, Haixiang Zang, Zhinong Wei, Tao Ding, Ruiqi Xu, Guoqiang Sun
Summary: This study proposes an end-to-end short-term forecasting model that uses satellite images to predict solar power generation by learning cloud motion characteristics. With its optimized deep learning architecture, the model outperforms other methods in prediction results and learning capability, making it suitable for PV plants in different areas.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Green & Sustainable Science & Technology
Tao Ding, Xiaosheng Zhang, Runzhao Lu, Ming Qu, Mohammad Shahidehpour, Yuankang He, Tianen Chen
Summary: This paper proposes a virtual energy storage (VES) model to accommodate renewable energy under a special market regulation. A multi-stage distributionally robust optimization (MSDRO) model is set up to address temporal uncertainties. A stochastic dual dynamic programming method is employed to efficiently solve the model.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Engineering, Multidisciplinary
Zekai Wang, Tao Ding, Wenhao Jia, Chenggang Mu, Can Huang, Joao P. S. Catalao
Summary: This article proposes an innovative integrated power and hydrogen distribution system restoration model to address multiple outages caused by natural disasters. The model considers repair crews and mobile battery-carried vehicles for line repairs and critical power load support. Network reconfiguration and aerodynamic law-based dynamic hydrogen flow model are also incorporated. The mixed-integer linear program is verified on a 33-bus-48-node system, showing the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Chunyang Liu, Hengxu Zhang, Mohammad Shahidehpour, Quan Zhou, Tao Ding
Summary: This paper proposes a two-layer real-time scheduling model for optimizing microgrid operations based on future cost function. The effectiveness of the model is validated through experiments.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Lilin Cheng, Haixiang Zang, Zhinong Wei, Tao Ding, Guoqiang Sun
Summary: This study proposes a graphical learning framework for intra-hour PV power prediction. By simulating cloud motion, a directed graph is generated to represent pixel values from historical images. A spatial-temporal graph neural network (GNN) is used to process the graph. Comparing with conventional deep-learning-based models, GNN is more flexible and able to handle dynamic regions of interest (ROIs), while reducing redundancy of image inputs and slightly improving prediction accuracy.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Haixiang Zang, Xin Jiang, LiLin Cheng, Fengchun Zhang, Zhinong Wei, Guoqiang Sun
Summary: This study proposes a modeling method for estimating solar radiation specifically for sites that cannot afford to install solar radiation measurement equipment. The method analyzes the correlation between meteorological factors and solar radiation, as well as the correlation of adjacent sites, and introduces a hybrid model to estimate solar radiation. The results of the study show that the proposed hybrid model outperforms benchmark models, and the method can be applied to different regions without solar radiation measurement.
Article
Green & Sustainable Science & Technology
Tao Ding, Yuge Sun, Can Huang, Chenlu Mu, Yuqi Fan, Jiang Lin, Yining Qin
Summary: Clean energy heating electrification programs have the potential to reduce carbon emissions from fossil fuel consumption. This study examines the cost competitiveness of clean energy heating technologies under different dynamic mechanisms. The results suggest that individual heating programs are more cost-efficient in urban areas with existing heating networks, and these programs are expected to dominate the market by 2050.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Green & Sustainable Science & Technology
Lilin Cheng, Haixiang Zang, Zhinong Wei, Fengchun Zhang, Guoqiang Sun
Summary: Deep-learning solar power forecast models have improved prediction precision but sacrificed interpretability. This study aims to increase confidence in the practical engineering utilization of deep-learning-based intelligent models for solar power forecasting through evaluation and analysis of a typical model.
Article
Automation & Control Systems
Hongji Zhang, Tao Ding, Junjian Qi, Wei Wei, Joao P. S. Catalao, Mohammad Shahidehpour
Summary: In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system's vulnerability to cascading outages. The proposed model combines Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve learning precision. The model is tested on several systems to show its effectiveness.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Xiaoyue Zhang, Xinghua Liu, Tao Ding, Peng Wang
Summary: This article investigates the resiliently distributed fixed-time control of frequency recovery and power allocation in a multi-terminal high voltage direct current (MTDC) system against denial-of-service (DoS) attacks. A novel distributed security control scheme is proposed, which introduces attack detection method and communication repair mechanism to restore the paralyzed topology caused by DoS attacks. The proposed control scheme can not only realize frequency restoration but also accomplish active power sharing under DoS attacks. The resilient stability of the proposed scheme is proved by Lyapunov-Krasovskii stability theory, and case studies are conducted to demonstrate the effectiveness of the proposed controller.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
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.
