Article
Computer Science, Information Systems
Mohamed Louzazni, Heba Mosalam, Daniel Tudor Cotfas
Summary: The NARX model, based on neural networks and time series analysis, is proposed for one-month forecast of produced power from photovoltaic modules. It is powerful in emulating nonlinear dynamic state-space models and is important in complex process control.
Article
Green & Sustainable Science & Technology
A. Mellit, A. Massi Pavan, V. Lughi
Summary: This paper develops and compares different types of deep learning neural networks (DLNN) for short-term output PV power forecasting, showing very good accuracy in one-step prediction within a 1-minute time horizon and acceptable results in multi-step prediction.
Article
Green & Sustainable Science & Technology
Elissaios Sarmas, Evangelos Spiliotis, Efstathios Stamatopoulos, Vangelis Marinakis, Haris Doukas
Summary: This paper proposes a meta-learning method to improve short-term deterministic forecasts of PV systems by blending the base forecasts of multiple DL models. Results indicate that different base models perform best at different PV plants, and meta-learning can improve accuracy by up to 5% over the most accurate base model per plant.
Article
Energy & Fuels
Yutong He, Qingzhong Gao, Yuanyuan Jin, Fang Liu
Summary: This research proposes a hybrid model combining CNN and BiLSTM for accurately estimating the energy output of a short-term photovoltaic system. The model utilizes Pearson correlation analysis to screen meteorological factors highly correlated with PV power output, and employs CNN for feature extraction and BiLSTM for timing prediction. Experimental results based on data from a specific region in China demonstrate that this model reduces training time, improves prediction accuracy, and outperforms conventional models in forecasting effectiveness, meeting the practical demands of PV energy generation prediction.
Review
Green & Sustainable Science & Technology
Putri Nor Liyana Mohamad Radzi, Muhammad Naveed Akhter, Saad Mekhilef, Noraisyah Mohamed Shah
Summary: Advancements in renewable energy technology have reduced consumer dependence on conventional energy sources. Solar energy is a sustainable source of power generation with various factors affecting its performance. Machine learning and neural networks play a crucial role in accurately forecasting the output power of photovoltaic systems, taking into account different input parameters and time-step resolutions.
Article
Energy & Fuels
Nahid Sultana, S. M. Zakir Hossain, Salma Hamad Almuhaini, Dilek Dustegor
Summary: This study focuses on developing statistical and machine learning approaches for forecasting electricity demand in Ontario. The novel aspects of the study include identifying significant factors affecting electricity consumption, optimizing model hyperparameters using a Bayesian optimization algorithm, and comparing the performance of different models. The results show that the hybrid BOA-NARX model performs well in accurately predicting day-ahead electricity load forecasts.
Article
Green & Sustainable Science & Technology
Germanico Lopez, Pablo Arboleya
Summary: This study proposes a specific approach for accurate wind speed forecasting in complex terrain in the Ecuadorian Andes, using linear regression models, LSTM network, and NARX network. The comparison results indicate that the multivariable LSTM network shows the most precise values, demonstrating its importance in forecasting wind speed over complex terrain.
Article
Computer Science, Information Systems
Tao Fan, Tao Sun, Hu Liu, Xiangying Xie, Zhixiong Na
Summary: This paper introduces a novel Spatial-Temporal Genetic-based Attention Networks (STGANet) approach for PV power forecasting, which includes spatial-temporal module and genetic-based attention module to improve prediction performance.
Article
Energy & Fuels
Gang Li, Shunda Guo, Xiufeng Li, Chuntian Cheng
Summary: This study proposes a short-term forecasting approach for regional PV power plants based on bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN). The approach divides PV power plants with similar generation characteristics into the same output subregion using the k-means algorithm and selects representative power plants. A regional prediction model based on BiLSTM-CNN method is developed, which takes historical operation and meteorological data of the representative power plant as input and total subregional power generation as output. The approach is tested using real data from PV power plants in Chuxiong and Dali region, Yunnan province, China, and the results show that it effectively improves the short-term prediction accuracy of regional PV generation output.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Green & Sustainable Science & Technology
Nguyen Duc Tuyen, Nguyen Trong Thanh, Vu Xuan Son Huu, Goro Fujita
Summary: This article introduces a novel hybrid deep learning model named EDSACL for short-term PV maximum power prediction and validates its accuracy on two real-world datasets. The results demonstrate the superior performance of the proposed model compared to other predictive models.
IET RENEWABLE POWER GENERATION
(2023)
Article
Automation & Control Systems
Ying-Yi Hong, Yu-Hsuan Chan
Summary: This paper proposes a novel method that combines a hybrid convolutional neural network (CNN) with a fully-connected network for 24h-ahead electric load forecasting in power systems. The method utilizes Spearman's rank-order correlation for input size information and employs max and average pooling operations, as well as spatial and conventional dropouts, to extract data features and avoid overfitting. The proposed method optimizes the CNN structure and hyperparameters using enhanced elite-based particle swarm optimization (EEPSO) and optimizes the neural network parameters and weights using the Adam optimizer. Simulation results show that the proposed method outperforms various existing forecasting methods.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Thermodynamics
Deniz Korkmaz, Hakan Acikgoz, Ceyhun Yildiz
Summary: A novel photovoltaic power forecasting system using a deep Convolutional Neural Network and decomposition algorithm was proposed, showing superior performance compared to benchmark regression algorithms under various weather conditions.
INTERNATIONAL JOURNAL OF GREEN ENERGY
(2021)
Article
Green & Sustainable Science & Technology
Salahuddin Khan
Summary: Short-term load forecasting is crucial for the efficient management of electric systems and the development of reliable energy infrastructure. A novel integrated model combining wavelet transform decomposition, radial basis function network, and thermal exchange optimization algorithm was developed. The performance of this model was evaluated in two deregulated power markets and compared with various standard forecasting models.
Article
Computer Science, Information Systems
Georgios Tziolis, Andreas Livera, Jesus Montes-Romero, Spyros Theocharides, George Makrides, George E. Georghiou
Summary: This study presents a machine learning-based approach for short-term net load forecasting in solar-integrated microgrids. The proposed model demonstrates accurate and robust prediction performance, validated using historical net load data. The results show a 17.77% improvement over baseline models.
Article
Computer Science, Information Systems
Elissaios Alexios Papatheofanous, Vasileios Kalekis, Georgios Venitourakis, Filippos Tziolos, Dionysios Reisis
Summary: PV power production is highly variable due to meteorological effects. Researchers propose using computer vision and deep learning to forecast short-term irradiance using sky images. A method based on sun localization is introduced to improve the accuracy of irradiance estimation and the integration of models on an FPGA enables real-time control of PV production.
Review
Thermodynamics
B. Kalidasan, Muhammed A. Hassan, A. K. Pandey, Subramaniyan Chinnasamy
Summary: Hollow cavity receivers are a new type of structure that overcomes the drawbacks of conventional evacuated tube receivers. They have advantages such as extended working temperature ranges, reduced thermal losses, and improved efficiency. Future research should focus on optimization and economic assessments to promote the diversified development of the solar receiver industry.
APPLIED THERMAL ENGINEERING
(2023)
Article
Thermodynamics
Amr Kaood, Ibrahim O. Elhagali, Muhammed A. Hassan
Summary: This study introduces a compact and highly efficient mm-scale jet impingement cooling module and evaluates its performance through numerical characterization. The results indicate that CP3 outperforms other CPs in terms of thermal performance under different flow rates.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2023)
Article
Physics, Multidisciplinary
M. H. Badr, N. Kudrevatykh, M. A. Hassan, M. Moustafa, Y. S. Rammah, A. S. Abouhaswa, A. A. EL-Hamalawy
Summary: Sol-gel auto-combustion synthesis was used to synthesize Cd-Zn ferrite nanoparticles with varying Cd content. Characterization techniques including XRD, SEM, FTIR, and VSM were employed to study the physical properties of the samples. Cd-Zn spinel nanoferrites were formed, with an increase in lattice constant and grain size as Cd content increased. The decrease in saturation magnetization was attributed to weak interaction between cations.
Article
Energy & Fuels
Loiy Al-Ghussain, Muhammed A. Hassan, Ahmed Hamed
Summary: Compact installation of solar photovoltaic systems is necessary in urban regions with limited available solar irradiance. This study evaluates three installation schemes of dual-row near-wall ground-mounted PV systems. Mathematical models are developed to optimize the techno-economic performance based on tilt angles and reflector parameters. The results show favorability of the proposed schemes in terms of energy production and cost compared to the baseline configuration.
Article
Energy & Fuels
Loiy Al-Ghussain, Onur Taylan, Mohammad Abujubbeh, Muhammed A. Hassan
Summary: To address the increasing installation capacities of solar PV systems in desert areas, this study examines the impacts of ambient temperature, wind speed, dust accumulation, and cleaning frequency on energy production and optimal angles of PV panels. Results show higher energy production estimates using an isotropic model, and the cooling effect of wind speed reduces the operating temperature of thin-film panels. Cleaning the panels bi-monthly decreases annual energy production, and doubling the dust accumulation rate decreases energy production for all cases. The optimal tilt and azimuth angles vary within -3.0 degrees with dust accumulation rate.
Article
Green & Sustainable Science & Technology
Muhammed A. Hassan, Hindawi Salem, Nadjem Bailek, Ozgur Kisi
Summary: This study introduces random forest models to predict fuel consumption and emission rates of passenger cars in Greater Cairo, Egypt. The results demonstrate the reliability of the models in predicting fuel consumption and CO2, CO, and NOx emissions.
Article
Thermodynamics
B. Kalidasan, Subramaniyan Chinnasamy, A. K. Pandey, Muhammed A. Hassan, Kamal Sharma
Summary: The performance of a low-cost solar box cooker was investigated by adding fins to the cooking utensils. Different configurations of water-based cooking boxes were evaluated, and it was found that the use of fins improved cooking power and energy efficiency. The solar box cooker with hexagonal fins yielded the best results.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2023)
Article
Environmental Sciences
Samuel Chukwujindu Nwokolo, Nikolaos Proutsos, Edson L. Meyer, Chinedu Christian Ahia
Summary: This study used hybrid physics-based models to evaluate the effects of climate change and urban expansion on ecosystem productivity in China and India. The results showed that if global warming is limited to 1.5 degrees Celsius, the photosynthetically active radiation in both countries is projected to increase. Urban expansion had a greater impact than climate change in both countries, but the impact of climate change was greater in India than in China.
Article
Green & Sustainable Science & Technology
Muhammed A. Hassan, Aya Fouad, Khaled Dessoki, Loiy Al-Ghussain, Ahmed Hamed
Summary: This study investigates the performance of four double-glazed receivers by comparing the evacuated and non-evacuated annular spaces and varying the absorber tube diameter and glass shell diameter ratios. The results show that fully evacuating the receiver and using the smallest diameter of the three concentric cylinders lead to the highest energy and exergy efficiencies. However, evacuating only the inner annular space is sufficient to achieve almost the same performance level. Double glazing the receivers is advantageous at high operating temperatures of sCO2, reducing thermal losses compared to single-glazed receivers.
Article
Green & Sustainable Science & Technology
Ismail Bendaas, Kada Bouchouicha, Smail Semaoui, Abdelhak Razagui, Salim Bouchakour, Saliha Boulahchiche
Summary: This study evaluates the performance of a large-scale, grid-connected photovoltaic power plant (LS-PVPP) in a hot climate in Adrar, Algeria. Real-world data and simulations were used to assess various parameters, including yield, capacity factor, performance ratio, statistical indicators, and carbon emission savings. The results indicate a significant reduction in carbon emissions and provide valuable insights for researchers, photovoltaic project developers, and stakeholders to optimize performance and maximize environmental and economic benefits.
ENERGY FOR SUSTAINABLE DEVELOPMENT
(2023)
Article
Environmental Sciences
Nikolaos Proutsos, Dimitris Tigkas, Irida Tsevreni, Stavros G. Alexandris, Alexandra D. Solomou, Athanassios Bourletsikas, Stefanos Stefanidis, Samuel Chukwujindu Nwokolo
Summary: In this study, various empirical methods for potential evapotranspiration estimation were evaluated in Mediterranean urban green sites in Greece. The radiation-based methods and adjusted models performed better compared to temperature-based and mass transfer methods. Combination methods that require more data obtained the highest ranking scores.
Article
Energy & Fuels
Abul Abrar Masrur Ahmed, Nadjem Bailek, Laith Abualigah, Kada Bouchouicha, Alban Kuriqi, Alireza Sharifi, Pooya Sareh, Abdullah Mohammad Ghazi Al Khatib, Pradeep Mishra, Ilhami Colak, El-Sayed M. El-kenawy
Summary: This study evaluates the application of a new soft technique called Variational Mode Decomposition (VMD) in improving the accuracy of power consumption forecasts. The results show that the VMD-BiGRU and VMD-LSTM models outperform other models by 20% to 50% on all evaluation measures. Additionally, the study finds that VMD is most effective for short-to medium-term forecasts.
Article
Multidisciplinary Sciences
Ehab Gomaa, Bilel Zerouali, Salah Difi, Khaled A. El-Nagdy, Celso Augusto Guimaraes Santos, Zaki Abda, Sherif S. M. Ghoneim, Nadjem Bailek, Richarde Marques da Silva, Jitendra Rajput, Enas Ali
Summary: This study compares the performance of three models, GRNN, GPR, and MLP-PSO, in analyzing rainfall-runoff relationship and predicting runoff discharge. The MLP-PSO model achieves the best performance with the lowest RMSE. The study also explores the combination of EMD-HHT with GPR and MLP-PSO, and finds that the MLP-PSO-EMD model shows superior accuracy in streamflow prediction.
Article
Computer Science, Information Systems
Mehdi Jamei, Nadjem Bailek, Kada Bouchouicha, Muhammed A. Hassan, Ahmed Elbeltagi, Alban Kuriqi, Nadhir Al-Ansar, Javier Almorox, El-Sayed M. El-kenawy
Summary: This study evaluates the performance of different hybrid data-driven techniques in predicting daily global solar radiation in semi-arid regions. The testing phase outcomes demonstrate that the AR-RF model outperforms other hybrid models. The results also show that utilizing extraterrestrial solar radiation, relative humidity, wind speed, and ambient air temperatures as inputs leads to the most accurate predictions.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
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.