scholarly journals Probabilistic Forecasting of Nitrogen Dioxide Concentrations at an Urban Road Intersection

2018 ◽  
Vol 10 (11) ◽  
pp. 4213 ◽  
Author(s):  
Joanna Kamińska
Keyword(s):  
Wind Speed ◽  
Nitrogen Dioxide ◽  
City Planning ◽  
Probability Distributions ◽  
Large City ◽  
Probabilistic Forecasting ◽  
Deterministic Models ◽  
Principal Mechanism ◽  
Complex Processes ◽  
Input Variables

The concentration of nitrogen dioxide in the air along a major route in a large city is affected by very many factors, which are also interdependent. As an alternative to complicated deterministic models based on these complex processes, in this study a probabilistic model for predicting NO2 concentrations is proposed, using a simple accounting cluster-based method for determining probability distributions for tabulated values of ambient factors. Using the example of hourly values of NO2 concentration and data on wind speed and traffic flow for the main intersection in Wrocław (Poland), a model is constructed to predict the frequency of occurrence of concentrations in the form of a probability distribution, for given values of the input variables. The model was successfully verified on data for the first six months of 2018. A mean continuous rank probability score (CRPS) of 9.15 μg/m3 was obtained. In spite of the greater impact of traffic volume on urban NO2 concentrations, as measured by Pearson’s correlation coefficient, for instance, the model indicates that wind speed is also a very important factor—wind being the principal mechanism causing the evacuation of pollutants. This underlines the importance of sustainable city planning with regard to ensuring suitable conditions for the passage of air.

Selection of Input Variables for the Prediction of Wind Speed in Wind Farms Based on Genetic Algorithm

2011 ◽  
Vol 35 (6) ◽  
pp. 649-660 ◽  
Author(s):  
R. A. Gupta ◽  
Rajesh Kumar ◽  
Ajay Kumar Bansal
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Farms ◽  
Input Variables ◽  
Selection Of

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Evaluation of regional NWP results for sea ice covered Bothnia Bay (Baltic Sea) in winter 2020.

2021 ◽  
Author(s):  
Marta Wenta ◽  
Agnieszka Herman
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Sea Ice ◽  
Surface Temperature ◽  
Atmospheric Boundary Layer ◽  
Baltic Sea ◽  
Polar Regions ◽  
Near Surface ◽  
Accurate Representation ◽  
Complex Processes

<p>The ongoing development of NWP (Numerical Weather Prediction) models and their increasing horizontal resolution have significantly improved forecasting capabilities. However, in the polar regions models struggle with the representation of near-surface atmospheric properties and the vertical structure of the atmospheric boundary layer (ABL) over sea ice. Particularly difficult to resolve are near-surface temperature, wind speed, and humidity, along with diurnal changes of those properties. Many of the complex processes happening at the interface of sea ice and atmosphere, i.e. vertical fluxes, turbulence, atmosphere - surface coupling are poorly parameterized or not represented in the models at all. Limited data coverage and our poor understanding of the complex processes taking place in the polar ABL limit the development of suitable parametrizations. We try to contribute to the ongoing effort to improve the forecast skill in polar regions through the analysis of unmanned aerial vehicles (UAVs) and automatic weather station (AWS) atmospheric measurements from the coastal area of Bothnia Bay (Wenta et. al., 2021), and the application of those datasets for the analysis of regional NWP models' forecasts. </p><p>Data collected during HAOS (Hailuoto Atmospheric Observations over Sea ice) campaign (Wenta et. al., 2021) is used for the evaluation of regional NWP models results from AROME (Applications of Research to Operations at Mesoscale) - Arctic, HIRLAM (High Resolution Limited Area Model) and WRF (Weather Research and Forecasting). The presented analysis focuses on 27 Feb. 2020 - 2 Mar. 2020, the time of the HAOS campaign, shortly after the formation of new, thin sea ice off the westernmost point of Hailuoto island.  Throughout the studied period weather conditions changed from very cold (-14℃), dry and cloud-free to warmer (~ -5℃), more humid and opaquely cloudy. We evaluate models’ ability to correctly resolve near-surface temperature, humidity, and wind speed, along with vertical changes of temperature and humidity over the sea ice. It is found that generally, models struggle with an accurate representation of surface-based temperature inversions, vertical variations of humidity, and temporal wind speed changes. Furthermore, a WRF Single Columng Model (SCM) is launched to study whether specific WRF planetary boundary layer parameterizations (MYJ, YSU, MYNN, QNSE), vertical resolution, and more accurate representation of surface conditions increase the WRF model’s ability to resolve the ABL above sea ice in the Bay of Bothnia. Experiments with WRF SCM are also used to determine the possible reasons behind model’s biases. Preliminary results show that accurate representation of sea ice conditions, including thickness, surface temperature, albedo, and snow coverage is crucial for increasing the quality of NWP models forecasts. We emphasize the importance of further development of parametrizations focusing on the processes at the sea ice-atmosphere interface.</p><p> </p><p>Reference:</p><p>Wenta, M., Brus, D., Doulgeris, K., Vakkari, V., and Herman, A.: Winter atmospheric boundary layer observations over sea ice in the coastal zone of the Bay of Bothnia (Baltic Sea), Earth Syst. Sci. Data, 13, 33–42, https://doi.org/10.5194/essd-13-33-2021, 2021. </p><p><br><br><br><br><br><br></p>

scholarly journals Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia

Data in Brief ◽  
2019 ◽  
Vol 27 ◽  
pp. 104753 ◽  
Author(s):  
Guillermo Valencia Ochoa ◽  
José Núñez Alvarez ◽  
Marley Vanegas Chamorro
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Probability Distributions ◽  
Data Set

scholarly journals Assessment of wind energy potential at Kuwaiti Islands by statistical analysis of wind speed data

2018 ◽  
Vol 51 ◽  
pp. 01001
Author(s):  
Khaled Al-Salem ◽  
Waleed Al-Nassar
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Lower Layer ◽  
Probability Distributions ◽  
Clean Energy ◽  
Energy Potential ◽  
Wind Speed Data ◽  
The North ◽  
Energy Conversion Systems ◽  
Wind Energy Potential

Kuwait possesses a potential of renewable energy, such as solar and wind energy. Wind energy is an alternative clean energy source compared to fossil fuel, which pollute the lower layer of the atmosphere. In this study, statistical methods are used to analyze the wind speed data at Mubarak port (at Bubiyan Island), Failaka Island and Um-AlMaradim Island; which are located respectively in the north, mid and south of Kuwait territorial waters. Wind speed is the most important parameter in the design and study of wind energy conversion systems. The wind speed data were obtained from the Costal Information System Database (CIS) at Kuwait Institute for Scientific Research [1, 2 and 3]over a thirty seven years period, 1979 to 2015. In the present study, the wind energy potential of the locations was statistically analyzed based on wind speed data, over a period of thirty seven years. The probability distributions are derived from the wind data and their distributional parameters are identified. Two probability density functions are fitted to the probability distributions on a yearly basis. The wind energy potential of the locations was studied based on the Weibull and the Rayleigh models.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

scholarly journals Probabilistic photovoltaic power forecasting model based on deterministic forecasts

2020 ◽  
Vol 152 ◽  
pp. 01003
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Pedro J. Zorzano-Santamaria ◽  
Montserrat Mendoza-Villena ◽  
Eduardo Garcia-Garrido
Keyword(s):  
Genetic Algorithm ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Deterministic Models ◽  
Conditional Quantile ◽  
Normal Distributions ◽  
Probabilistic Forecasts ◽  
Mixture Of Normal Distributions ◽  
Quantile Regression Method ◽  
Power Forecasting

This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.

scholarly journals Use of Bayesian population viability analysis to assess multiple management decisions in the recovery programme for the Endangered takahe Porphyrio hochstetteri

Oryx ◽  
2013 ◽  
Vol 47 (1) ◽  
pp. 144-152 ◽  
Author(s):  
Danilo Hegg ◽  
Darryl I. MacKenzie ◽  
Ian G. Jamieson
Keyword(s):  
Probability Distributions ◽  
Management Strategies ◽  
Population Viability Analysis ◽  
Experimental Manipulation ◽  
Population Modelling ◽  
Deterministic Models ◽  
Logistic Regression Models ◽  
Viability Analysis ◽  
Management Actions ◽  
Captive Rearing

AbstractPopulation modelling is an invaluable tool for identifying effective management strategies for threatened species whose populations are too small for experimental manipulation. Recently developed Bayesian approaches allow us to combine deterministic models with probability distributions to create stochastic models that account for uncertainty. We illustrate this approach in the case of the takahe Porphyrio hochstetteri, an Endangered flightless rail, which is supported by one of New Zealand's costliest recovery programmes. Using mark–recapture and logistic regression models implemented in a Bayesian framework we calculated demographic parameters for a fully stochastic population model based on 25 years of data collected from the last wild population of takahe in the Murchison Mountains, Fiordland. Our model results show that stoat trapping, captive rearing and cross-fostering of eggs/chicks in wild pairs all have a positive effect on takahe demography. If it were not for these management actions the Fiordland population would probably be declining (λ = 0.985; confidence interval, CI = 0.39–1.08), with a non-negligible risk of quasi-extinction (P = 16%) within 20 years. The captive rearing of eggs and chicks has been the main factor responsible for the positive growth observed during the last decade but in the future expanding stoat trapping to cover the entire Murchison Mountains would be the single most beneficial management action for the takahe population (λ = 1.038; CI = 0.86–1.10), followed by captive rearing (λ = 1.027; CI = 0.85–1.09).

scholarly journals Framework for evaluating risk of limited sight distance for permitted left-turn movements: case study

2016 ◽  
Vol 43 (4) ◽  
pp. 369-377 ◽  
Author(s):  
Ahmed Osama ◽  
Tarek Sayed ◽  
Said Easa
Keyword(s):  
Reliability Analysis ◽  
Probability Distributions ◽  
Video Data ◽  
Analysis Framework ◽  
Left Turn ◽  
Sight Distance ◽  
Time Gap ◽  
Input Variables ◽  
The City

A reliability analysis framework is used to evaluate the risk of limited sight distance for permitted left-turn movements due to the presence of opposing left-turn vehicles. Two signalized intersection approaches in the city of Surrey were used as case studies for the framework. Geometric and traffic video data was collected and analyzed using a computer vision tool to extract the input variables probability distributions. The data was used in the reliability analysis where first-order and Importance Sampling methods were performed. The analysis showed that the probability of non-compliance was considerable at one approach due to its large left-turn lane offset. The analysis also showed that the probability of non-compliance increased substantially when the obstacle vehicle was a bus rather than a passenger car. Moreover, the time gap had a higher impact on the probability of non-compliance compared to speed. Strategies were suggested to overcome the high probability of non-compliance.

Sign in / Sign up

Export Citation Format

Share Document