Wind Farm Site Research Articles

Spatial Optimization for Energy Island Location and Cable Routing Considering Offshore Zoning

The energy hub concept presents a compelling opportunity to channel electricity from offshore wind farms to the grid, facilitating cross-border energy trading and conversion. However, one of the main challenges lies in the strategic identification of a suitable energy island location and the establishment of an interconnected cable routing path. Part of the complexity arises from the need to navigate offshore zoning regulations and accommodate various spatial uses within the offshore area. This paper exhibits a case study to explore the spatial optimization for a potential offshore energy hub in the North Sea. It aims at finding out an optimal spatial configuration of the offshore energy hub, such that the island location and the associated cable routing do not infringe upon keep-out zones while strategically positioning the hub closer to high-capacity wind farms. To achieve this goal, detailed geographical data incorporating offshore zoning information is leveraged for spatial optimization. The Dijkstra’s algorithm is then applied to identify the optimal island location and cable routing path. The effectiveness of this spatial optimization methodology is demonstrated through a case study involving offshore wind farm sites in the North Sea. Given the real geographical data, simulation results underscore the efficacy of the Dijkstra’s algorithm in determining the optimal energy hub layout. In particular, an optimal spatial design is achieved, based on cable lengths, asset capacities, offshore zoning and island/platform location for a potential offshore energy hub in the North Sea while ensuring compliance with keep-out zoning regulations.

Evaluating Offshore Wind as a Catalyst for Trinidad and Tobago’s Petrochemical Sector

This study introduces a comprehensive analysis of offshore wind resource potential in Trinidad and Tobago, leveraging both the Wind Atlas Methodology (WAM) and the numerical wind atlas methodologies to address the region’s sparse wind measurement data. Utilizing atmospheric re-analysis data, specifically the ERA5 dataset, in conjunction with the Weather Research and Forecasting (WRF) model, a generalized wind climates (GWCs) for Trinidad and Tobago was generated. These GWCs, refined with topographical and roughness data, guide the siting of offshore wind farms within the Exclusive Economic Zone (EEZ), considering water depths and proximate onshore terrain influences. The study quantifies the economic feasibility of offshore wind through both a levelized cost of electricity (LCOE) analysis and by evaluating the value of redirected natural gas to the petrochemical industry. The LCOE, though currently higher than subsidized domestic electricity rates, is projected to decrease significantly by 2035. Notably, the value of displaced natural gas for petrochemical production offers substantial economic benefits, with potential payback periods for offshore wind investments well under a decade when considering 2021 methanol and ammonia prices. These findings underscore the strategic significance of offshore wind in Trinidad and Tobago’s energy mix. By transitioning to renewable energy sources, the nation can mitigate reliance on fossil fuels for power generation while optimizing natural gas usage in high-value sectors.

Prediction of Compression Coefficients Based on Machine Learning: A Case of Offshore Wind Farm Site

Prediction of Compression Coefficients Based on Machine Learning: A Case of Offshore Wind Farm Site

A GIS-based multi-criteria decision-making approach (GIS-MCDM) for determination of the most appropriate site selection of onshore wind farm in Adana, Turkey

AbstractEnergy stands as an indispensable global need, yet the finite nature of rapidly depleting fossil fuel reserves poses environmental challenges. To address this, numerous nations are shifting toward sustainable energy sources to foster environmental well-being while satisfying their energy requirements. Among these alternatives, wind energy emerges as a particularly efficient option. This study focuses on identifying optimal sites for installing wind power plants in Adana province through the integration of the analytic hierarchy process (AHP) and geographic information system (GIS). Within this framework, a comprehensive set of 15 criteria was delineated along with their respective sub-criteria. It is thought that the criteria used in the study will be useful not only for Adana province but also for other provinces that are suitable for wind turbine placement and planned to be built. The outcome of this investigation delineates potential areas in Adana province deemed suitable for establishing wind power plants. As a result, analysis reveals that 9.94% of the study area is conducive to establishing a wind farm (WF), while 51.66% is deemed moderately suitable. Notably, the districts of Feke, Tufanbeyli, Saimbeyli, and Pozantı emerge as particularly well-suited for this purpose. This study aims to contribute to the existing literature by identifying precise and suitable areas by combining GIS and AHP in the WF site selection process. In the study, a new research perspective is presented by taking into account the uncertainty in the site selection process and the concept of sustainability in three different dimensions: technical, economic, and environmental, thus aiming to guide decision-makers for future WF projects. In addition, it is thought that the methods and criteria used in the study will also be guiding for future WF site selection studies. Graphical abstract

Optimal Wind Farm Siting Using a Fuzzy Analytic Hierarchy Process: Evaluating the Island of Andros, Greece

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity.

The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects

Abstract. The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.

Lessons learned in wind-driven desalination systems in the Canary Islands: Useful knowledge for other world islands

This study addresses the imperative transition towards renewable energy for the powering of desalination technologies, focusing particularly on island contexts. The Canary Archipelago serves as a unique wind energy-desalination laboratory, with numerous experimental projects conducted since the late 1980s. The lessons learnt that are identified in this paper highlight the critical factors influencing project success or failure, including the modernization of desalination plants, the optimal selection of wind farm sites and turbine technology, adequate resources for operating and maintenance tasks, and optimal battery sizing to minimize system shutdowns. In this context, reverse osmosis emerges as the most advantageous technology for variable operating conditions. System interfaces between wind turbines and desalination units are also discussed and it is identified that further research is needed for mechanical or hydrostatic interface systems, while electrical interface systems suit low-medium water demand areas. Recommendations are given for system design and maintenance with the aim of providing valuable information for other island communities grappling with the water-energy nexus and climate change mitigation.

An efficient solver for large-scale onshore wind farm siting including cable routing

Existing planning approaches for onshore wind farm siting and grid integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an exact approach for the integrated layout and cable routing problem of onshore wind farm planning using the Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. In selected regions of Germany, the trade-offs between minimizing costs and landscape impact of onshore wind farm siting are investigated. Although our case studies show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, grid integration must be simultaneously optimized to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision-making and help optimize wind farms in large regions in the future.

Study on the anisotropic small strain stiffness of marine clay in China using bender elements

Small strain stiffness anisotropy of marine soils plays an important role in determining the natural frequency of offshore wind turbine structures. The small strain stiffness anisotropy is contingent on stress anisotropy and fabric anisotropy, with the latter being assessable via stiffness anisotropy under isotropic stress conditions. In this study, the small strain stiffness of marine clay obtained from the site of an offshore wind farm on the southeast coast of China was measured using a bender element. The effects of void ratio, effective mean pressure, and overconsolidation ratio on small strain stiffness were investigated and the related empirical equations were proposed for predicting the small strain stiffness of the marine clay. Furthermore, the small strain stiffness anisotropy of the marine clay under isotropic consolidation was also investigated. The findings revealed remarkable anisotropy in the small strain stiffness of the tested marine clay.

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.

Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms

The process of wind farm site selection involves complex considerations spanning environmental, economic, and social factors, often handled through multi-criteria decision making (MCDM) methods. However, MCDM approaches face several challenges. To address these, integrating machine learning (ML) techniques offers promise. Nevertheless, existing studies have yet to employ a standalone ML-based approach for this purpose. This study seeks to pioneer an explainable ML-based framework for automated wind farm site selection. The framework incorporates feature selection techniques, seven ML algorithms, statistical tests, and a local explainability method. The experimental area was chosen as Balıkesir province, which hosts 13% of Türkiye's installed wind energy capacity. According to the prediction performances of the ML models, Extreme Gradient Boosting-XGBoost has the highest accuracy (0.9607), followed by Light Gradient Boosting Machine-LightGBM (0.9580), Random Forest-RF (0.9518), Histogram-based Gradient Boosting-HGB (0.9387), Classification and Regression Trees-CART (0.8946), Logistic Regression-LR (0.8856), and Naive Bayes-NB (0.8456). Additionally, McNemar’s tests revealed statistically significant differences among ML models. Based on the explanations provided by SHapley Additive exPlanations, wind speed, distance to transmission lines, distance to protected zones, and elevation were the top contributing criteria. Moreover, analysis revealed regions with elevated wind speeds, higher elevations, and closer proximity to transmission lines and protected areas as favorable for wind farm installation. The study's outcomes indicate strong agreement (97%) between current wind turbine locations and those identified as suitable by ML models. Within Balıkesir province, our research has identified a significant number of highly suitable areas where wind turbines have not yet been installed. These findings underscore the critical importance of these locations for future investments in wind farm development. The proposed framework could be highly efficient for wind farm siting studies in provinces that share similar characteristics with the study area used in this study offering a robust tool for future wind farm siting studies. Finally, the study contributes to achieving UN SDG 7 by employing ML to refine wind farm site selection, thereby facilitating the transition towards sustainable and universally accessible energy.

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.

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Offshore wind energy and fisheries: Sustainable development goals, enterprise practices, and fishermen's perspectives

AbstractThis study comprises two essential components: a scoping review and interviews with fishermen, both aimed at understanding the Sustainable Development Goals (SDGs) and their relevance to Offshore Wind Energy (OWE) enterprises, particularly in mitigating impacts on local fisheries. Key findings from the scoping review indicate a scarcity of research explicitly dedicated to OWE enterprises' practices concerning. The review's findings emphasize the need for further research to understand the rationale behind these decisions and to explore how OWE enterprises can optimize their practices to address SDG 2. The second part of the study involves interviews with fishermen near proposed offshore wind farm sites in Taiwan. These interviews revealed that fishermen's primary concerns revolve around SDGs 2, 8, 14, and 17. However, OWE enterprises do not prioritize SDG 2, even though fishermen express concerns related to food security, proposing the adoption of marine ranching to enhance seafood supply. Moreover, SDG 17 stands out as a priority for fishermen, while OWE enterprises overlook connections with fisheries associations and fishermen. In conclusion, this study highlights the complexity of the SDG‐related issues surrounding OWE enterprises and their impact on local fisheries.

Rain erosion atlas for wind turbine blades based on ERA5 and NORA3 for Scandinavia

Leading edge erosion on wind turbine blades is a common issue, particularly for wind turbines placed in regions characterized by high wind speeds and precipitation. This study presents the development of a rain erosion atlas for Scandinavia and Finland, based on ERA5 reanalysis and NORA3 mesoscale model data on rainfall intensity and wind speed over five years. The IEA 15 MW reference wind turbine is used as an example to evaluate impingement water impact and erosion onset time for a commercial coating material. The damage progression is modeled by combining the wind speed and rainfall data with an empirical damage model that relates impinged water (H) as a function of impact velocity to the time of erosion onset. Comparative analyses at two weather station locations show that NORA3 data more accurately aligns with measurements in terms of power spectral density, mean wind speed, rainfall, and erosion prediction than ERA5. NORA3-based atlas layers offer finer spatial detail and predict shorter erosion onset times over land compared to ERA5, particularly in complex terrain. Conversely, the ERA5-based atlas suggests a shorter onset of erosion offshore. Based on NORA3 data, erosion onset time is estimated at 5 years on average for Baltic Sea wind farm sites and 3.2 years for sites in the North Sea.

A novel socio-techno-environmental GIS approach to assess the contribution of ground-mounted photovoltaics to achieve climate neutrality in Germany

The German government’s goal of achieving climate neutrality by 2045 and increasing the use of renewable energy, particularly wind and solar power, has public support. However, the local siting of wind and solar farms is highly controversial due to land-use conflicts. We have developed a comprehensive Geographic Information System (GIS) model that integrates criteria from the literature to identify optimal locations for ground-mounted photovoltaic (GM-PV). The model is based on a holistic approach considering energy-related factors and broader environmental and climate aspects. The unique feature of the model is its integration of socio-technical and environmental criteria, including a novel approach to achieving a dual land-use climate benefit by protecting erosion-prone soils and conserving soil carbon in areas prone to organic matter decomposition and associated CO2 release. Our multi-criteria GM-PV model includes 12 restrictions and five suitability criteria representing key socio-technical, economic and environmental aspects. Scenario analysis highlights the sensitivity of Germany’s GM-PV potential to defined criteria. In Scenario 1, with minimal constraint, 14.2 % of Germany’s land is available for GM-PV, decreasing to 1.9 % in the highly constrained Scenario 12. Suitable areas (>10 ha) account for 0.4 % of Germany’s land area, which could cover 8.1 % of Germany’s electricity consumption in 2045 in the highly restricted scenario. The spatial distribution results show that the innovative approach of the model changes the preference of GM-PV locations based on factors such as solar radiation in the south and soil carbon storage in the north of Germany. The application of restriction and suitability criteria significantly reduces the potential for GM-PV in Germany but also potential pitfalls for decision-makers. The GIS model can be applied in regional context to integrate stakeholder values and generate region-specific scenarios, as criteria and thresholds can be easily adapted to regional characteristics, preferences, and concerns, including those related to agriculture, biodiversity, and social acceptance.

Wind farm site selection using GIS-based multicriteria analysis with Life cycle assessment integration

The sustainability of wind power plants depends on the selection of suitable installation locations, which should consider not only economic and technical factors including manufacturing and raw materials, but also issues pertaining to the environment. In the present study, a novel methodology is proposed to determine the suitable locations for wind turbine farms by analyzing from the environmental perspective. In the methodology, the life cycle assessment (LCA) of wind turbines is incorporated into the decision process. The criteria are ranked using analytical hierarchy process (AHP). The study area is chosen as the western region of Türkiye. The obtained suitability map reveals that wind speed is not the sole criterion for selecting a site for wind turbine farms; other factors, such as bird migration paths, distance from urban areas and land use, are also crucial. The results also reveal that constructing wind power plants in the vicinity of İzmir, Çanakkale, Istanbul, and Balıkesir in Türkiye can lead to a reduction in emissions. İzmir and its surrounding area show the best environmental performance with the lowest CO2 per kilowatt-hour (7.14 g CO2 eq/kWh), to install a wind turbine due to its proximity to the harbor and steel factory across the study area. Çanakkale and the northwest region of Türkiye, despite having high wind speeds, are less environmentally favorable than İzmir, Balıkesir, and Istanbul. The findings of LCA reveal that the nacelle and rotor components of the wind turbine contribute significantly (43–97%) to the environmental impact categories studied, while the tower component (0–36%) also has an impact.

Evolution of the European offshore renewable energy resource under multiple climate change scenarios and forecasting horizons via CMIP6

The design of the different offshore renewable energy (ORE) technologies depends on the characteristics of wind/wave resources. However, these characteristics are not stationary under climate change. In this study the evolution of European offshore wave/wind resources is assessed under mid- and high-emissions scenarios based on the Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and three forecasting horizons including the near (2023–2032), mid (2041–2050) and long term (2091–2100). The novelties lie in: (i) the concurrent analysis of wind and wave resources, (ii) the use of data with a 3-hour temporal resolution, and (iii) the local-scale statistical analysis. Results show significant variations along the 21st century, with an overall decline in average wind and wave conditions. Importantly, and somewhat counter-intuitively, much of the Atlantic coast of continental Europe experiences increasing extremes in the high-emissions scenario. For the local-scale statistical analysis, the focus is on five wind farm sites, commissioned or in planning. In the high-emissions scenario, the 99th and 99.99th percentiles decrease in three of them and increase in the other two. The combination of decreasing averages and increasing extremes presents the worst-case scenario for ORE technology developers.

Energy justice: Lessons from offshore wind farm siting conflicts in South Korea

Given the growing salience of energy transition conflicts, policymakers need better tools to explain, prevent, and resolve them. The concept of energy justice highlights the normative direction of the energy system, and therefore, we examined the offshore wind farm siting conflict in Yeonggwang-gun, South Korea, through the lens of energy justice. In this study, we interviewed representatives of local stakeholder groups relevant to offshore wind farms and conducted text-mining analysis to extract the primary opinions of stakeholders. Through text-analysis, four primary factors were identified and discussed in relation to energy justice: gillnet fishing, consultation with the fishing village cooperative, damage by private developers, and a government-led conditional agreement. This mixed-method approach showed that the voices of fishing communities correlate with the four tenets of energy justice—distributional, procedural, recognition, and restorative justice. We discuss how recognition and restorative justice explain energy transition conflict under inadequate policy arrangements in South Korea, where little has been investigated in energy justice literature. By filling the gaps in energy transition conflict, we suggest inclusive policy strategies, revisiting the meaning and utility of participatory planning, scoping methods for Environmental and Social Impact Assessments, and just transition.

Ekstrapolacija vertikalne brzine vetra korišćenjem statističkih pristupa

The wind power industry has experienced a significant increase and popularity in recent times, and the latest statistics indicate that this sector is still thriving. However, one of the essential steps in developing wind energy projects is finding suitable sites for wind farms, which involves understanding the nature of wind speed, wind direction, terrain, and environmental impacts. To predict the wind energy production over the expected lifespan of a wind farm, vertical wind speed extrapolation to the hub height of the wind turbine is necessary. Therefore, this study presents a comprehensive evaluation of seven statistical approaches for vertical wind speed extrapolation, including Generalized Linear Models (GLM), Linear Regression (LR), Support Vector Machines (SVM), Generalized Additive Models (GAM), Gaussian Process Regression (GPR), Regression Tree (RT), and Ensemble Regression (ER). The accuracy of these methods is assessed using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Normalized MSE (NMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and R-squared (R2). The study concludes that, on average, GLM performs the best out of all seven statistical methods.