Wind Farm Locations Research Articles

Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States

Abstract Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs. Significance Statement Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.

Study of underwater sound propagation and attenuation characteristics at the Yangjiang offshore wind farma

The rapid growth of offshore wind farms has become a global priority, with both new and total installed capacities increasing sharply. Consequently, underwater noise generated with these developments has garnered significant attention. This study investigated the signals produced by 5.5 MW wind turbines at the Yangjiang offshore wind farm, focusing on various distances and depths. Results showed that the primary energy of the underwater noise was concentrated below 1500 Hz. At the same distance, deeper waters had lower noise levels than shallower waters. The discrete spectrum near the wind farm has a dominant frequency of 44 Hz. The peak sound pressure levels reach 93.76 dB at a depth of 10 m and 81.55 dB at 20 m, measured 50 m from the turbine. Horizontally, the sound pressure level of the dominant frequency decreased consistently as the distance from the wind farm increased. The sound transmission loss within 1 km is less than 10 dB, reaching 16.39 dB at 4 km, with noise levels nearing ambient ocean noise. A segmented spectral wide-angle parabolic equation was used to simulate the spatial sound field of the underwater noise, considering seabed topography. The noise propagation and attenuation models were validated against the measured data. Understanding noise propagation and attenuation with distance is crucial for selecting suitable offshore wind farm locations. Mitigating the impact of elevated underwater noise on sound-dependent species is essential for their survival.

Partisan winds: Group-level polarization and issue-framing propel attitudes about local wind farms

Political polarization is an obstacle to public support and effective communication for renewable energy projects. Depolarizing messages can be helpful, but it is difficult to determine where to concentrate efforts until social scientists first disentangle the effects of unconscious issue-based and conscious group norm-based polarization processes. This study investigates the extent to which attitudes towards wind energy development are polarized in the United States, focusing on attitudes about local wind farms. We tested different frames in a survey with 1300 U.S. participants, combining implicit and explicit attitude measures to measure unconscious and conscious attitudes towards nearby wind farms, respectively. Our findings suggest that explicit attitudes towards wind farms are more polarized than implicit attitudes, emphasizing the role of conscious processes in shaping attitudes. Furthermore, perceptions of within-party support significantly influence explicit attitudes, indicating the importance of group norm-based polarization in this context. While our framing interventions aimed at addressing issue-based polarization yielded mixed results, the moderating effect of perceived within-party support underscores the potential efficacy of interventions targeting group-level processes. Regarding policy implications, our findings highlight the importance of considering both issue-based and group norm-based polarization when developing and implementing communication strategies for garnering local support for nearby renewable energy developments.

Balancing Stakeholders’ Perspectives for Sustainability: GIS-MCDM for Onshore Wind Energy Planning

This study supports Jamaica’s renewable energy implementation strategies by providing updated wind atlases and identifying suitable locations for future wind farms. Using a GIS-based Analytic Hierarchy Process with multi-criteria decision-making (AHP-MCDM), this research integrates stakeholders’ opinions, environmental considerations, and technical factors to assess land suitability for wind energy development. The analysis reveals that Jamaica has the potential to increase its wind power output by 8.99% compared to the current production of 99 MW. This expansion could significantly contribute to offsetting fossil fuel-based energy consumption and reducing carbon dioxide emissions. It identifies sites across several parishes, including Westmoreland, Clarendon, St. Mary, and St. James, as highly suitable for utility-scale wind farm development. By providing detailed spatial information and estimated energy outputs, this research offers valuable insights for energy planners, investors, and policymakers to create sustainable energy policies and advance Jamaica’s 50% renewable energy goal by 2030.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

A Spatial Decision-Support System for Wind Farm Site Selection in Djibouti

The escalating energy demand in Djibouti requires the investigation of renewable energy sources, with wind energy emerging as a promising solution. To ensure the long-term efficiency and sustainability of wind energy projects, it is imperative to determine suitable sites for wind farm construction. When selecting a suitable site for a wind farm, there are multiple criteria to consider, such as wind velocity, ground slope, and distance to urban areas. Nevertheless, the current body of the literature reveals that no previous research has been conducted to explore an approach which involves multiple criteria to determine suitable sites for wind farms in Djibouti, as opposed to solely considering wind energy potential. This study proposes a spatial decision-support system to address the research gap in the selection of wind farm sites. Seven criteria are simultaneously evaluated in this system, including wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The CRITIC (Criteria Importance Through Intercriteria Correlation) method is used to objectively calculate the weights of the criteria. According to the results of performing the CRITIC method, wind velocity and distance to energy transmission networks were determined to be the most important criteria, while ground slope and land use were determined to be the least important criteria in comparison to others. A final suitability map showing the possible locations of wind farms in Djibouti was generated by considering the said criteria and their respective weights. The final suitability map reveals that the most suitable sites for the development of wind farms in Djibouti are located in the northeastern area between Obock and Khor-Angor, the southeastern area encompassing Lakes Ghoubet and Bara, and the southwestern area stretching from Lake Abbe to the Hanlé region. Using the proposed spatial decision-support system, decision makers would be empowered to make strategic and well-informed decisions when selecting the most suitable site for a wind farm in Djibouti.

Towards advanced sustainable criteria for choosing the best site for collecting solar energy in cities using multi-criteria GIS

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms.

GIS-Based Optimal Siting of Offshore Wind Farms to Support Zero-Emission Ferry Routes

To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities.

Wind Farms’ Location and Geographical Proximity as a Key Factor in Sustainable City Development: Evidence from Poland

In this study, the potential impact of wind farm locations on the sustainable development of cities in Poland was evaluated, considering the availability of wind-generated electricity. We analyzed 37 cities in Poland with populations over 100,000. Research indicates that wind farms located 30–80 km from large cities perform best in terms of generating capacity, while greater distances increase transmission costs and energy losses. In Poland, wind farms are primarily situated in the northwestern regions, posing challenges for energy transmission to the southern cities, which are the main centers of energy consumption. The findings show that wind farms with the highest generating capacity are generally about 50 km from major cities. Key factors influencing wind farm locations include technical criteria, economic feasibility, environmental impact, public opinion, and the availability of transmission networks. Sustainable development of wind farms requires strategic cooperation between urban and rural municipalities, joint spatial planning, coordinated land acquisition, and the exchange of know-how.

Innovations in Offshore Wind: Reviewing Current Status and Future Prospects with a Parametric Analysis of Helical Pile Performance for Anchoring Mooring Lines

This study examines the current status and future potential of the offshore wind sector. Offshore wind is pivotal in transitioning to a low-carbon society and meeting rising energy demands, despite being capital-intensive. The industry aims to develop larger-scale wind farms in deeper ocean locations, with projections indicating significant cost reductions. To explore deeper ocean areas, specialized foundations like floating platforms moored to the seabed are required. This study proposes helical piles anchored in the seabed as a method to secure mooring lines. Using Plaxis 3D, a parametric examination was conducted on helical piles with two plates: one fixed at the pile’s toe and the other varying in position between 0.5 and 13 m from the seabed surface. Load inclination angles (0, 20, 40, and 60 degrees) were used to simulate mooring line loads. Results indicate the optimal Zh/Z ratios for maintaining load-bearing capacity and stability: 0.12 (10 mm movements), 0.22 (25 mm), and 0.26 (50 mm) for small shaft diameters; and 0.34 (10 mm), 0.38 (25 mm), and 0.46 (50 mm) for large shaft diameters. These findings highlight the importance of specific load inclination angles based on shaft diameter and allowable movement for effective performance.

Spatial planning offshore wind energy farms in California for mediating fisheries and wildlife conservation impacts

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to plans of development across the WEA and inside three leases recently acquired by the energy industry for prospective development. Finally, we integrated the sector response data into an analytical framework for mitigating sector tradeoffs with novel spatial planning solutions (maps of wind farm size, location, and configuration) that optimally maximize value to the emergent energy sector (MW power) while minimizing impacts to historical (fisheries and wildlife) sectors. We found that western sites in the WEA had the highest potential power production concurrent with the lowest impact on the historical sectors, revealing the eastern lease to be less efficient at optimally balancing the sector's objectives relative to the development of the central or western leases or the optimal spatial plans identified in the tradeoff analysis. Within a lease, tradeoff analysis found spatial planning able to generate out-sized savings in fisheries value with only modest losses in MW power – for example, by avoiding development in just 5% of the eastern lease to preserve nearly half its fisheries value and still generate 95% its total power potential. Small-scale development opportunities (e.g., a pilot project) with significant power potential and no fisheries impact were also identified, in this case by placing turbines in an area in the western lease with no fisheries value and high power production potential. These plans would also have a relatively low impact on the wildlife conservation sectors, due to decreases in vulnerability levels of both seabird and cetacean populations to turbines going from east to west across the WEA. Our results can inform site evaluation and permitting processes for wind energy development in the Morro Bay WEA. We also expect the tradeoff analysis framework we developed to provide a simple and actionable analytical tool for supporting marine spatial planning of offshore wind energy and other emerging blue economy activities from a balanced perspective that values emerging uses of marine resources alongside existing socio-economic and conservation interests.

Wind farm site selection using GIS-based mathematical modeling and fuzzy logic tools: a case study of Burundi

Introduction: The electricity generated from nuclear plants and petroleum-based products has a negative influence on the environment as a whole. It has shown the utility to search out and promote the utilization of renewable, environmentally friendly, and sustainable energy sources such as solar, wind, and geothermal. Nowadays, Wind energy resource has quickly emerged as the world’s fastest-growing energy source.Methods: However, the selection of the most suitable places for developing a wind farm is a crucial challenge that can be seen as a problem of site selection, which involves numerous conflicting variables. Therefore, it is classified as an MCDM (multi-criteria decision-making) problem. The main objective of this research is to determine the best locations in Burundi for the installation of wind farms. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. This study considers several key factors when determining the optimal location for a wind farm. These factors include wind speed, slope, proximity to the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map.Results and Discussion: The obtained results indicate that 20.91% of the whole study area is suitable nevertheless, only 1.96% is tremendously suitable for wind turbine placement. The western part of Burundi is the optimal area for constructing a wind farm, and the most is in Lake Tanganyika.

Towing Analysis and Validation of a Fully Assembled Floating Offshore Wind Turbine Based on an Experimental Study

The offshore wind sector is moving into deep waters and using floating platforms to harness the higher wind speeds in exposed locations. There are various floating platform types currently in development, but semi-submersibles are considered the most prominent early movers. Such floaters need to be towed to and from wind farm locations for installation, special cases of repair and decommissioning. As with any other offshore activity, metocean limits exist for towing operations which can impact the development of a wind farm. It is important to calculate the motion and loads of the platform before commencing the towing operations and to check whether they exceed the defined limits to enable safe execution. In this paper, two approaches using two different numerical tools to predict the motion of a fully assembled floating wind platform under tow are presented and compared. A potential flow-based method derived from a low forward speed approach and a hybrid approach combining potential flow and Morison equation methods are investigated, and the numerical predictions are compared and validated against experimental results. Both methods demonstrate accurate predictions, depending on the wave condition and towing speed, albeit differing in execution time and the simplicity of the simulation setup. The first method was found to provide good predictions of the motion in low-speed (0.514–1.543 m/s) towing conditions. The second method provides better results for all the towing speeds and wave heights. As the wave height and towing speed increase, deviations from experiments were observed, signifying non-linear phenomena that are difficult to analyse using the mentioned potential-flow-based methods.

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Spatial modeling sensitivity analysis: Copula selection for wind speed dependence

As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs.

Short-Term Marine Wind Speed Forecasting Based on Dynamic Graph Embedding and Spatiotemporal Information

Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network—long short-term memory joint framework (DGE-GAT-LSTM) to estimate wind speed at numerous stations by considering their spatio-temporal information properties. To begin, the buoys that are pertinent to the target station are chosen based on their geographic position. Then, the local graph structures connecting the stations are represented using cosine similarity at each time interval. Subsequently, the graph neural network captures intricate spatial characteristics, while the LSTM module acquires knowledge of temporal interdependence. The graph neural network and LSTM module are sequentially interconnected to collectively capture spatio-temporal correlations. Ultimately, the multi-step prediction outcomes are produced in a sequential way, where each step relies on the previous predictions. The empirical data are derived from direct measurements made by NDBC buoys. The results indicate that the suggested method achieves a mean absolute error reduction ranging from 1% to 36% when compared to other benchmark methods. This improvement in accuracy is statistically significant. This approach effectively addresses the challenges of inadequate information integration and the complexity of modeling temporal correlations in the forecast of ocean wind speed. It offers valuable insights for optimizing the selection of offshore wind farm locations and enhancing operational and management capabilities.

On the design of a cup anemometer performance simulator. From the wind speed to the output data.

This paper describes some important aspects of the development of a cup anemometer performance simulator. This tool aims to simulate the different phenomena which affect the different subsystems of the anemometer, in order to develop different strategies to improve the accuracy of the data obtained through post-processing or design changes. Bearing in mind that the cup anemometer is the most widely used wind speed sensor in the wind energy sector (to control wind generators and to analyse the future economic revenue of wind farms in certain locations), the relevance of the present study should be highlighted. The software is designed in Simulink® and is divided in 4 modules representing the different subsystems of an anemometer from the actual wind speed to the output data. The first two modules of the simulator are detailed in this paper, which consist of the Rotation Rate Module and the Pulse Generating Module. The first module outputs the anemometer’s rotor angular position with time, which depends on the wind speed of the anemometer. Oscillations of the rotation rate caused by steady state harmonic accelerations are taken into account. The Pulse Generating Module simulates the optoelectronic system of the anemometer and creates the pulse train signal based on the rotor’s angular position, taking into account the manufacturing and eccentricity errors of the system. The architecture of the modules is detailed, and related with the phenomena which is intended to simulate.

Co-optimisation of wind and solar energy and intermittency for renewable generator site selection

Sites for the construction of wind and solar farms have typically been chosen to maximise total energy generation of an individual site, but rarely consider the intermittency of the renewable resource available at each location. As more renewable generation is added to electricity grids around the world, this intermittency is rapidly becoming a major factor constraining the volume of renewable generation that can be added cost-effectively, as additional fast-response storage or dispatchable generation must compensate for periods of low renewable generation. We present a statistical approach to selecting wind and solar generation sites that assesses energy and intermittency of individual wind, solar and co-sited wind plus solar farm locations, allowing energy and intermittency to be given weight when selecting sites for new generation. A new multi-objective pareto-front approach to identifying high-performing renewable generation sites that allows for optimising multi-site selection using the median (energy) and median absolute difference (intermittency) of historical weather resource is proposed. This technique is then applied to a 30-year, hourly, Australian weather reanalysis dataset to show the potential improvement over the fleet of wind and solar farms currently operating in the Australian National Energy Market. Finally, an analysis of potential sites for optimal offshore wind and combined offshore wind and solar is presented. The proposed wind-only and solar-only sites show an average energy increase of 9% (solar, 67 sites), 28% (wind, 50 sites) more energy for the same level of intermittency, as compared to the existing generation sites. Three existing combined wind and solar sites were compared to the best three proposed sites, which showed a 3-16% energy increase for the same intermittency, or a 2-11% intermittency decrease for the same energy generated.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

Nowcasting of Wind Speed using Support Vector Regression. Experiments with Time Series from Gran Canaria

The aim of this paper is to describe and evaluate a proposal for nowcasting wind speed for wind farm locations from historical time series, based on the method of regression by support vectors. To show the improvement over other methods, we used the ANEMOS Project standard evaluation protocol for forecasting against three reference models to compare, referred to a statistical approach: persistence, autoregressive and autoregressive moving average models. The obtained results show a good performance of the proposed method and how beat the classical reference models.