Roughness sub-layer wind speed model for tropical wooded areas

2022 ◽  
pp. 0309524X2110500
Author(s):  
Gustavo Richmond-Navarro ◽  
Mariana Montenegro-Montero ◽  
Pedro Casanova-Treto ◽  
Franklin Hernández-Castro ◽  
Jorge Monge-Fallas
Keyword(s):  
Artificial Intelligence ◽  
Wind Speed ◽  
Functional Relationship ◽  
Polynomial Function ◽  
Evolutionary Search ◽  
Search Technique ◽  
Humidity Sensors ◽  
Wind Speeds ◽  
The One ◽  
Future Work

There are few reports in the literature regarding wind speed near the ground. This work presents a model for wind speed from 4 m above the ground, based on year-round measurements in two meteorological towers. Each tower is equipped with anemometers at five heights, as well as thermometers and pressure and relative humidity sensors. The data is processed using Eureqa artificial intelligence software, which determines the functional relationship between variables using an evolutionary search technique called symbolic regression. Using this technique, models are found for each month under study, in which height and temperature are the variables that most affect wind speed. The model that best predicts the measured wind speeds is then selected. A polynomial function directly proportional to height and temperature is identified as the one that provides the best predictions of wind speed on average, within the rough sub-layer. Finally, future work is identified on testing the model at other locations.

scholarly journals Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

2017 ◽  
Vol 32 (6) ◽  
pp. 2217-2227 ◽  
Author(s):  
Siri Sofie Eide ◽  
John Bjørnar Bremnes ◽  
Ingelin Steinsland
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Model Averaging ◽  
Strong Wind ◽  
Ensemble Forecasts ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
The One

Abstract In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

2018 ◽  
pp. 28-50
Author(s):  
S. G. Ignatiev ◽  
S. V. Kiseleva
Keyword(s):  
Energy Storage ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Demand ◽  
Diesel Generator ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

scholarly journals Analysis of Performance of the Wind-Driven Pulverizing Aerator Based on Average Wind Speeds in the Conditions of Góreckie Lake

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2796
Author(s):  
Andrzej Osuch ◽  
Ewa Osuch ◽  
Stanisław Podsiadłowski ◽  
Piotr Rybacki
Keyword(s):  
Mathematical Model ◽  
Wind Speed ◽  
Water Pump ◽  
Average Wind Speed ◽  
Flow Rates ◽  
Wind Speeds ◽  
Average Wind ◽  
Flow Through ◽  
Analysis Of Performance ◽  
Research Period

In the introduction to this paper, the characteristics of Góreckie lake and the construction and operation of the wind-driven pulverizing aerator are presented. The purpose of this manuscript is to determine the efficiency of the pulverizing aerator unit in the windy conditions of Góreckie Lake. The efficiency of the pulverization aerator depends on the wind conditions at the lake. It was necessary to conduct thorough research to determine the efficiency of water flow through the pulverization segment (water pump). It was necessary to determine the rotational speed of the paddle wheel, which depended on the average wind speed. Throughout the research period, measurements of hourly average wind speed were carried out. It was possible to determine the efficiency of the machine by developing a dedicated mathematical model. The latest method was used in the research, consisting of determining the theoretical volumetric flow rates of water in the pulverizing aerator unit, based on average hourly wind speeds. Pulverization efficiency under the conditions of Góreckie Lake was determined based on 6600 average wind speeds for spring, summer and autumn, 2018. Based on the model, the theoretical efficiency of the machine was calculated, which, under the conditions of Góreckie Lake, amounted to 75,000 m3 per year.

scholarly journals Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1587
Author(s):  
Krzysztof Wrobel ◽  
Krzysztof Tomczewski ◽  
Artur Sliwinski ◽  
Andrzej Tomczewski
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Power ◽  
Power Plants ◽  
Control Method ◽  
Wind Power Plant ◽  
Switched Reluctance ◽  
Wind Speeds ◽  
Wind Speed Distribution ◽  
And Control

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

scholarly journals A Novel Sea Surface Roughness Parameterization Based on Wave State and Sea Foam

2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Difu Sun ◽  
Junqiang Song ◽  
Xiaoyong Li ◽  
Kaijun Ren ◽  
Hongze Leng
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Drag Coefficient ◽  
Sea Surface ◽  
High Wind Speed ◽  
Wave State ◽  
Wind Speeds ◽  
Sea Surface Roughness ◽  
Extreme Wind ◽  
The Impact

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

scholarly journals Topographic Speed-Up Effects and Observed Roof Damage on Bermuda following Hurricane Fabian (2003)

2013 ◽  
Vol 28 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Craig Miller ◽  
Michael Gibbons ◽  
Kyle Beatty ◽  
Auguste Boissonnade
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Surface Wind ◽  
Open Water ◽  
Clear Trend ◽  
Wind Speeds ◽  
Research Division ◽  
Hurricane Wind ◽  
Layer Flow ◽  
Observed Damage

Abstract In this study the impacts of the topography of Bermuda on the damage patterns observed following the passage of Hurricane Fabian over the island on 5 September 2003 are considered. Using a linearized model of atmospheric boundary layer flow over low-slope topography that also incorporates a model for changes of surface roughness, sets of directionally dependent wind speed adjustment factors were calculated for the island of Bermuda. These factors were then used in combination with a time-stepping model for the open water wind field of Hurricane Fabian derived from the Hurricane Research Division Real-Time Hurricane Wind Analysis System (H*Wind) surface wind analyses to calculate the maximum 1-min mean wind speed at locations across the island for the following conditions: open water, roughness changes only, and topography and roughness changes combined. Comparison of the modeled 1-min mean wind speeds and directions with observations from a site on the southeast coast of Bermuda showed good agreement between the two sets of values. Maximum open water wind speeds across the entire island showed very little variation and were of category 2 strength on the Saffir–Simpson scale. While the effects of surface roughness changes on the modeled wind speeds showed very little correlation with the observed damage, the effect of the underlying topography led to maximum modeled wind speeds of category 4 strength being reached in highly localized areas on the island. Furthermore, the observed damage was found to be very well correlated with these regions of topographically enhanced wind speeds, with a very clear trend of increasing damage with increasing wind speeds.

Climate Conditions at Perennially Ice-Covered Lake Untersee, East Antarctica

2015 ◽  
Vol 54 (7) ◽  
pp. 1393-1412 ◽  
Author(s):  
Dale T. Andersen ◽  
Christopher P. McKay ◽  
Victor Lagun
Keyword(s):  
Wind Speed ◽  
Austral Summer ◽  
Solar Flux ◽  
East Antarctica ◽  
Daily Maximum ◽  
Mean Annual Temperature ◽  
Secondary Source ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Strong Winds

AbstractIn November 2008 an automated meteorological station was established at Lake Untersee in East Antarctica, producing a 5-yr data record of meteorological conditions at the lake. This dataset includes five austral summer seasons composed of December, January, and February (DJF). The average solar flux at Lake Untersee for the four years with complete solar flux data is 99.2 ± 0.6 W m−2. The mean annual temperature at Lake Untersee was determined to be −10.6° ± 0.6°C. The annual degree-days above freezing for the five years were 9.7, 37.7, 22.4, 7.0, and 48.8, respectively, with summer (DJF) accounting for virtually all of this. For these five summers the average DJF temperatures were −3.5°, −1.9°, −2.2°, −2.6°, and −2.5°C. The maximum (minimum) temperatures were +5.3°, +7.6°, +5.7°, +4.4°, and +9.0°C (−13.8°, −12.8°, −12.9°, −13.5°, and −12.1°C). The average of the wind speed recorded was 5.4 m s−1, the maximum was 35.7 m s−1, and the average daily maximum was 15 m s−1. The wind speed was higher in the winter, averaging 6.4 m s−1. Summer winds averaged 4.7 m s−1. The dominant wind direction for strong winds is from the south for all seasons, with a secondary source of strong winds in the summer from the east-northeast. Relative humidity averages 37%; however, high values will occur with an average period of ~10 days, providing a strong indicator of the quasi-periodic passage of storms across the site. Low summer temperatures and high wind speeds create conditions at the surface of the lake ice resulting in sublimation rather than melting as the main mass-loss process.

scholarly journals Atmospheric Circulation Effects on Wind Speed Variability at Turbine Height

2007 ◽  
Vol 46 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Katherine Klink
Keyword(s):  
Wind Speed ◽  
Pressure Gradient ◽  
Large Scale ◽  
Ground Level ◽  
The Arctic ◽  
Circulation Index ◽  
Regression Equations ◽  
Change Models ◽  
Wind Speeds ◽  
The North

Abstract Mean monthly wind speed at 70 m above ground level is investigated for 11 sites in Minnesota for the period 1995–2003. Wind speeds at these sites show significant spatial and temporal coherence, with prolonged periods of above- and below-normal values that can persist for as long as 12 months. Monthly variation in wind speed primarily is determined by the north–south pressure gradient, which captures between 22% and 47% of the variability (depending on the site). Regression on wind speed residuals (pressure gradient effects removed) shows that an additional 6%–15% of the variation can be related to the Arctic Oscillation (AO) and Niño-3.4 sea surface temperature (SST) anomalies. Wind speeds showed little correspondence with variation in the Pacific–North American (PNA) circulation index. The effect of the strong El Niño of 1997/98 on the wind speed time series was investigated by recomputing the regression equations with this period excluded. The north–south pressure gradient remains the primary determinant of mean monthly 70-m wind speeds, but with 1997/98 removed the influence of the AO increases at nearly all stations while the importance of the Niño-3.4 SSTs generally decreases. Relationships with the PNA remain small. These results suggest that long-term patterns of low-frequency wind speed (and thus wind power) variability can be estimated using large-scale circulation features as represented by large-scale climatic datasets and by climate-change models.

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