Recent advances and applications of machine learning in the variable renewable energy sector

Energy Reports, Год журнала: 2024, Номер 12, С. 5044 - 5065

Опубликована: Ноя. 8, 2024

Язык: Английский

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Spatial assessment of utility-scale solar photovoltaic siting potential using machine learning approaches: A case study in Aichi prefecture, Japan

Applied Energy, Год журнала: 2025, Номер 383, С. 125329 - 125329

Опубликована: Янв. 16, 2025

Язык: Английский

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Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey

Energy Strategy Reviews, Год журнала: 2024, Номер 56, С. 101552 - 101552

Опубликована: Окт. 15, 2024

Язык: Английский

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Short-term wind power forecasting using integrated boosting approach

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Май 30, 2024

Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward large-scale integration renewable resources (RERs). Wind is a type RERs with vast potential no pollution associated it. The sustainable development goals: affordable clean energy, climate action, industry, innovation infrastructure, can be achieved by integrating wind into existing power systems. However, will bring instability challenges due to its intermittent nature. Mitigating these necessitates implementation effective forecasting models. Therefore, we proposed novel integrated approach, Boost-LR, for hour-ahead forecasting. Boost-LR multilevel technique consisting non-parametric models, extreme gradient boosting (XgBoost), categorical (CatBoost), random forest (RF), parametric linear regression (LR). first layer uses algorithms that process data according their tree architectures pass intermediary forecast LR which deployed in two processes forecasts one models provide final predicted power. To demonstrate generalizability robustness study, performance compared individual CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) gated recurrent unit (GRU), Transformer Informer using root mean square error (RMSE), (MSE), absolute (MAE) normalized (NRMSE). Findings effectiveness as superior improvement MAE recorded 31.42%, 32.14%, 27.55% datasets Bruska, Jelinak, Inland farm, respectively CatBoost revealed second-best performing model. Moreover, study also reports literature comparison further validates

Язык: Английский

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Evaluation of Land Suitability for Construction in the Turpan–Hami Region Based on the Integration of the MaxEnt Model and Regional Planning

Land, Год журнала: 2025, Номер 14(4), С. 775 - 775

Опубликована: Апрель 4, 2025

Land resources are fundamental to regional economic development and ecological protection. As a critical tool for the scientific allocation of land resources, suitability evaluation plays pivotal role in achieving sustainable goals. This study integrates MaxEnt model with planning conduct multi-period construction Turpan–Hami region, aiming elucidate distribution patterns their driving mechanisms across different historical periods. By synthesizing natural geographic socioeconomic data, comprehensive framework was developed, enabling multi-temporal analysis from 2000 2023. The results revealed clear trend optimization within characterized by continuous expansion highly suitable areas significant reduction unsuitable areas, becoming increasingly balanced over time. population density, GDP, road density were identified as primary drivers distribution, exerting most substantial influence. Among environmental factors, Normalized Difference Vegetation Index (NDVI) imposed constraints on suitability, particularly ecologically sensitive areas. innovatively applied integrating it comprehensively assess spatial dynamic changes region. Furthermore, this research aligns closely policy frameworks, fully considering impacts agricultural protection policies explores optimized use strategies under guidance. findings provide robust foundation efficient enhancement

Язык: Английский

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Study on habitat suitability and ecological network of rare cranes in Poyang Lake National Nature Reserve

Ecological Indicators, Год журнала: 2025, Номер 174, С. 113480 - 113480

Опубликована: Апрель 18, 2025

Язык: Английский

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Exploring of Wind Energy Potential and Optimal Site Selection for Wind Energy Plants Installations in Erzurum/Turkey Based on Multicriteria Site Selection

International Journal of Energy Research, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Wind energy is a renewable, sustainable, and localized power source. These features motivate countries to adopt wind turbines, with many nations employing incentives enhance the installed capacity of these systems. The selection locations for turbine installations crucial optimizing performance augmenting capacity. This study examined 20 distinct places in province Erzurum as potential development installations. research identified prospective facilities Erzurum, Turkey, geographic information‐based multicriteria decision‐making (MCDM) methodology information system (GIS). plan incorporated topographical, structural, climatic, environmental factors, including velocity, land cover classification, gradient, closeness substations, transportation networks, communities, fault lines, grids, erosion areas. A farm suitability map was created using weighted process questionnaire assessed criteria maps, analytical hierarchy (AHP). result shows that 83.052% total area designated favorable, while 15.858% rated most favorable. highlighted criterion first time. investigation utilized 17 criteria. aim locate facility more suitable site by characteristics. In district‐level evaluations plant map, İspir occupies top position.

Язык: Английский

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Multi-Criteria Decision-Making for Wind Power Project Feasibility: Trends, Techniques, and Future Directions

Cleaner Engineering and Technology, Год журнала: 2025, Номер unknown, С. 100987 - 100987

Опубликована: Май 1, 2025

Язык: Английский

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Assessment of the criteria importance for determining solar panel site potential via machine learning algorithms, a case study Central Anatolia region, Turkey

Renewable Energy, Год журнала: 2024, Номер 239, С. 122145 - 122145

Опубликована: Дек. 10, 2024

Язык: Английский

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Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey

Sustainability, Год журнала: 2024, Номер 16(20), С. 8775 - 8775

Опубликована: Окт. 11, 2024

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments implement incentives augment their installed capacity turbines. Alongside augmenting turbines, identifying suitable locations for installation crucial optimizing turbine performance. This study aims evaluate potential sites power plant via a GIS, mapping technique. The Analytic Hierarchy Process (AHP) was employed assess locations, including both quantitative qualitative aspects that significantly impact farm suitability map. Utilizing GIS methodology, all datasets were examined through height raster transformations land surface temperature, density index, air pressure, humidity, speed, cover, solar radiation, aspect, slope, topographical characteristics, resulting in creation correlation between five-year meteorological data environmental parameters (wind direction, daily maximum minimum temperatures, relative average radiation duration, cloud pressure) influencing Iğdır province, Airport, Karakoyunlu, Aralık, Tuzluca districts, analyzed. If towers are at 1 km intervals across area roughly 858,180 hectares Igdir estimated GWh can be generated. GIS-derived map indicates plants located regions susceptible erosion.

Язык: Английский

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