Article
Energy & Fuels
A. Acevedo-Anicasio, E. Santoyo, D. Perez-Zarate, Kailasa Pandarinath, M. Guevara, L. Diaz-Gonzalez
Summary: A geochemometric study introduced eight new gas geothermometers and a computer program GaS_GeoT for predicting geothermal reservoir temperatures. Comparison of the new geothermometers with existing ones showed GasG(3) and GasG(1) to be the most consistent predictor models in VAPDR.
Article
Agriculture, Dairy & Animal Science
Hong-Seok Mun, Muhammad Ammar Dilawar, Shad Mahfuz, Keiven Mark B. Ampode, Veasna Chem, Young-Hwa Kim, Jong-Pil Moon, Chul-Ju Yang
Summary: The study evaluated the performance of a combined geothermal heat pump and solar system (GHPS) installed at a pig house, comparing it with traditional heating methods using fossil fuels. Results showed that the GHPS system is an efficient heating system, reducing electricity consumption and carbon dioxide gas concentration.
Review
Multidisciplinary Sciences
Zhi-Hua Zhou
Summary: This article introduces the concept and challenges of open-environment machine learning, as well as some technological advances and theoretical issues in this field.
NATIONAL SCIENCE REVIEW
(2022)
Article
Green & Sustainable Science & Technology
Cheng Chen, Yuhan Hu, Marimuthu Karuppiah, Priyan Malarvizhi Kumar
Summary: This paper introduces an Artificial Intelligence-based useful evaluation model (AIEM) for forecasting the impact of renewable energy and energy efficiency on the economy. The study aims to analyze, compare, and build a model utilizing artificial intelligence and specific economic indicators significant in economic prediction regarding renewable energy.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Energy & Fuels
Ali Javaid, Umer Javaid, Muhammad Sajid, Muhammad Rashid, Emad Uddin, Yasar Ayaz, Adeel Waqas
Summary: The feasibility of extracting hydrogen energy from wind in a suburban environment using artificial intelligence techniques was evaluated in this study. Experimental data was collected and models were trained to predict hydrogen production, with LSTM performing the best. The results showed that a 1.5 MW wind turbine could produce an average of 6.76 kg/day of hydrogen.
Review
Energy & Fuels
Lili Zhang, Jie Ling, Mingwei Lin
Summary: This paper provides a comprehensive bibliometric analysis to understand the evolution of Artificial Intelligence in Renewable Energy (AI&RE) research. The analysis shows that China is the most productive and influential country/region in this field, and AI-related technologies effectively address issues related to integrating renewable energy with the power system. The paper also discusses future research trends.
Review
Computer Science, Artificial Intelligence
Hristos Tyralis, Georgia Papacharalampous
Summary: Machine learning algorithms, particularly boosting algorithms, are extensively used in energy research for their flexibility and interpretability. This study provides insights into the properties of boosting algorithms and their potential for advancement in the energy field, emphasizing recent developments and relevant applications.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Enas Taha Sayed, A. G. Olabi, Khaled Elsaid, Muaz Al Radi, Concetta Semeraro, Mohammad Hossein Doranehgard, Mohamed Elrayah Eltayeb, Mohammad Ali Abdelkareem
Summary: Using various renewable energy resources to power desalination plants is an encouraging choice, especially in arid and remote areas. However, the unpredictable load demands and intermittency nature of these resources make designing such systems difficult. This work discusses various artificial intelligence techniques to enhance RES-powered desalination systems.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Green & Sustainable Science & Technology
Sana Mujeeb, Nadeem Javaid
Summary: Global warming, caused by Green House Gases (GHG) emissions, is a challenging issue. Decarbonization strategies, such as using Renewable Energy Sources (RES) and shifting consumption load, are adopted to address this issue. This paper presents a novel forecasting model that accurately predicts power system's carbon emissions using Spearman Correlation Analysis (SCA), Improved Shallow Denoising Autoencoder (ISDAE), Improved Particle Swarm Optimization (IPSO), and Deep Neural Network (DNN). The impacts of RES integration level on electricity price, consumption cost, and GHG emissions are also quantified. The proposed model outperforms SVM and MLR based carbon emission forecasting models in terms of accuracy.
IET RENEWABLE POWER GENERATION
(2023)
Review
Environmental Sciences
Yali Hou, Qunwei Wang
Summary: This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI), and energy. The results show that China has the highest number of publications, but the USA is the most influential country in the field. The Chinese Academy of Sciences plays a vital role in the collaboration network. The study also reveals the most productive journal and the most popular discipline in terms of publications.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Review
Environmental Sciences
Abhir Dashputre, Aluru Kaushik, Aparajita Pal, Dhruv Jariwala, Kriti Yadav, Manan Shah
Summary: Desalination is a proven method to obtain clean water from the ocean, but it requires a significant amount of energy. This paper focuses on thermoeconomically optimized multi-effect distillation and geothermal desalination systems, which utilize clean and renewable geothermal energy to produce power and desalinate water.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Review
Environmental Sciences
Mitul Prajapati, Manan Shah, Bhavna Soni
Summary: By 2040, India aims to complete its energy supply transformation to meet the country's growing energy demands. While there is significant potential in geothermal energy resources in India, they have not been fully utilized and require further research and development.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Energy & Fuels
Zhicheng Liu, Yipeng Liu, Hao Xu, Siyang Liao, Kefan Zhu, Xinxiong Jiang
Summary: This paper studies the economic dispatch of power systems based on deep deterministic policy gradient (DDPG) and builds an algorithm framework. The experimental results show that the proposed algorithm is highly adaptable to random fluctuations of renewable energy.
Review
Green & Sustainable Science & Technology
Tanveer Ahmad, Dongdong Zhang, Chao Huang, Hongcai Zhang, Ningyi Dai, Yonghua Song, Huanxin Chen
Summary: This study focuses on the use of AI techniques in the energy sector, exploring AI's advantages in solar and hydrogen power generation, supply and demand management control, and recent technological advances. The findings show that AI is becoming a key enabler in enhancing operational performance and efficiency in the energy industry to remain competitive in a cutthroat environment.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Artificial Intelligence
M. Talaat, M. H. Elkholy, Adel Alblawi, Taghreed Said
Summary: This paper provides a comprehensive review of the integration of renewable energy sources (RESs), including various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as the use of artificial intelligence in the integration. It also discusses optimization techniques to reduce the total cost of integrated energy sources and up-to-date methods to improve the performance of the electrical grid. A case study demonstrates that the use of artificial intelligence improves the accuracy of operation and enables effective and accurate prediction control of the integrated system. Various optimization techniques combined with ANN are used to select the best hybrid model, with PSO showing a fast convergence rate and reaching a minimum error of 1.10% in 3367.50 seconds.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Software Engineering
Gianpaolo Coro, Giancarlo Panichi, Pasquale Pagano, Erico Perrone
Summary: Text mining involves analyzing text to extract high-quality information. NLPHub is a distributed system that adopts an Open Science approach, recognizing spatiotemporal events, keywords, and a large set of named entities to improve performance by combining output.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Multidisciplinary Sciences
Gianpaolo Coro, Anna Nora Tassetti, Enrico Nicola Armelloni, Jacopo Pulcinella, Carmen Ferra, Mario Sprovieri, Fabio Trincardi, Giuseppe Scarcella
Summary: The COVID-19 pandemic presents an important opportunity to study the dynamics of fishing effort and the industry's response to regulations. This research focuses on the main fishing fleets in the Adriatic Sea and measures their response to reduced fishing hours. The study also examines the beneficial effects of lockdowns on endangered and protected species in the Adriatic Sea. Additionally, the research finds that the Adriatic fishing fleets generally behave like stocks under significant stress.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Software Engineering
Massimiliano Assante, Leonardo Candela, Donatella Castelli, Roberto Cirillo, Gianpaolo Coro, Andrea Dell'Amico, Luca Frosini, Lucio Lelii, Marco Lettere, Francesco Mangiacrapa, Pasquale Pagano, Giancarlo Panichi, Tommaso Piccioli, Fabio Sinibaldi
Summary: This article introduces the concept and importance of virtual research environments, emphasizing the effectiveness of the co-creation driven approach in developing these environments. It also provides some usage indicators as examples.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Education & Educational Research
Gianpaolo Coro, Serena Bardelli, Armando Cuttano, Nicoletta Fossati
Summary: Training through simulation in neonatology using sophisticated devices can effectively train specialized medical teams. Tailoring simulation to trainees' emotional status and communication abilities is crucial for successful clinical interventions. This study presents an automatic workflow that detects potentially ineffective communication during simulation sessions and provides useful information for trainers. It achieves a detection accuracy of 64% in a challenging speech-processing context.
EDUCATION AND INFORMATION TECHNOLOGIES
(2022)
Article
Information Science & Library Science
Costantino Thanos, Carlo Meghini, Valentina Bartalesi, Gianpaolo Coro
Summary: The current scientific context is characterized by digitization and data infrastructures, allowing researchers to explore hidden relationships and discover new information. The use of Linked Data and Semantic Web technologies enables a new exploratory approach to scientific research.
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
(2022)
Article
Ecology
Gianpaolo Coro, Pasquale Bove, Anton Ellenbroek
Summary: This paper presents an approach to assess potential habitat changes of eight marine species in the Adriatic Sea in 2020. The results suggest that the combination of climate change and the pandemic could have heterogeneous effects on habitat distributions.
ECOLOGICAL INFORMATICS
(2022)
Review
Geochemistry & Geophysics
Andrea Dini, Pierfranco Lattanzi, Giovanni Ruggieri, Eugenio Trumpy
Summary: Italy is not known for lithium production, despite its famous lithium mineral specimens from Elba Island. However, there is potential for lithium resources in magmatic rocks in Sardinia, Calabria, and the Central Alps. The Tertiary-Quaternary magmatic rocks in Italy have higher lithium content than normal arc igneous rocks worldwide. Additionally, subsurface fluids such as high-enthalpy geothermal fields and lower-temperature thermal waters may contain significant amounts of lithium. Future exploration should focus on these potential sources.
Article
Environmental Sciences
Gianpaolo Coro, Pasquale Bove, Enrico Nicola Armelloni, Francesco Masnadi, Martina Scanu, Giuseppe Scarcella
Summary: International scientific fishery survey programmes collect samples of fish stocks and use them to estimate stock status. However, missed survey locations are common in long-term programme data. This study proposes a statistical and machine learning model to fill gaps in survey data and provide robust estimates for stock assessment and management.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Environmental Sciences
Giuseppe Scarcella, Silvia Angelini, Enrico Nicola Armelloni, Ilaria Costantini, Andrea De Felice, Stefano Guicciardi, Iole Leonori, Francesco Masnadi, Martina Scanu, Gianpaolo Coro
Summary: The COVID-19 pandemic has had a significant impact on the seafood supply chain and fishing activity in the Mediterranean Sea. A study has examined the effects of the pandemic on fish stocks in the Adriatic Sea subareas. The results show that most commercially exploited fish stocks have experienced a small but significant improvement in terms of biomass and reduction in fishing mortality, except for cuttlefish and pink shrimp.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Remote Sensing
Gianpaolo Coro, Pasquale Bove
Summary: COVID-19 spread models should consider dynamic factors such as human mobility and interaction, and incorporate environmental parameters for accurate predictions and analysis. This article presents a high-resolution global probability map using surface air temperature, precipitation, and CO2 as parameters, which successfully predicted 87% of countries with high infection rates in 2020 and 80% of overall low and high infection-rate countries.
ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS
(2022)
Article
Geography, Physical
Valentina Bartalesi, Gianpaolo Coro, Emanuele Lenzi, Pasquale Pagano, Nicolo Pratelli
Summary: Digital maps are a valuable tool for storytelling about territories, particularly when supplemented with data on cultural, societal, and ecological aspects, in order to convey emotional messages about the territory as a whole. Story maps, which are interactive online narratives, can go beyond traditional maps by incorporating text, images, videos, and other multimedia information. This study presents a semi-automatic workflow that utilizes natural language processing and Wikidata services to extract key concepts and geospatial coordinates from textual documents, creating enriched story maps that can be openly published online for exploration and analysis. Numerical evaluation demonstrated that experts found these story maps effective for describing territories and communicating with stakeholders and citizens.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Computer Science, Artificial Intelligence
Gianpaolo Coro, Serena Bardelli, Armando Cuttano, Rosa T. T. Scaramuzzo, Massimiliano Ciantelli
Summary: This paper describes an open-source workflow for infant-cry detection, which can identify high-quality infant-cry samples and be used to build and populate a database. It utilizes automated analysis and classification methods and is cost-effective.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Gianpaolo Coro, Pasquale Bove, Kathleen Kesner-Reyes
Summary: This paper presents a comprehensive collection of marine ecological and ecological-niche model data, including environmental, geophysical, and marine-related data. The dataset consists of 2132 raster data for 58 different parameters, covering regional and global scales in ESRI-GRID ASCII format. The data were sourced from various heterogeneous repositories and include both open data and data specifically created for this publication. The dataset includes global-scale data, data at different resolutions and temporal aggregations, and forecasts for different future scenarios.
Article
Computer Science, Theory & Methods
Costantino Thanos, Carlo Meghini, Valentina Bartalesi, Gianpaolo Coro
Summary: This paper describes a new approach to knowledge creation in data-intensive science by exploiting existing relationships between diverse types of datasets. This approach enables the acquisition of new insights and promotes the exploration of data patterns within a scientific data space. The limitations of the Linked Open Data (LOD) technology for acquiring new insights are discussed, and a new approach for dynamically creating data patterns in a research data space is proposed.
JOURNAL OF BIG DATA
(2023)
Article
Food Science & Technology
Gianpaolo Coro, Lorenzo Sana, Carmen Ferra, Pasquale Bove, Giuseppe Scarcella
Summary: Monitoring fishery activity is crucial for resource planning and ensuring the sustainability of fisheries. Large fishing vessels use Automatic Identification Systems (AIS) or Vessel Monitoring Systems (VMS) to constantly communicate their positions. Processing and integrating these data with other fisheries data allows for exploring the relationships between socio-economic and ecosystem assets in marine areas, which is essential for fishery monitoring.
FRONTIERS IN SUSTAINABLE FOOD SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad
Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang
Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu
Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang
Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.
JOURNAL OF CLEANER PRODUCTION
(2024)
Article
Green & Sustainable Science & Technology
Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He
Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.
JOURNAL OF CLEANER PRODUCTION
(2024)
