scholarly journals AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0)

2019 ◽  
Vol 12 (7) ◽  
pp. 2915-2940
Author(s):  
Pascal Horton
Keyword(s):  
Climate Change ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Swiss Alps ◽  
Raspberry Pi ◽  
Statistical Relationship ◽  
Impact Studies ◽  
Operational Forecasting ◽  
Climate Studies

Abstract. Analog methods (AMs) use synoptic-scale predictors to search in the past for similar days to a target day in order to infer the predictand of interest, such as daily precipitation. They can rely on outputs of numerical weather prediction (NWP) models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated useful potential for application in several contexts. AtmoSwing is open-source software written in C++ that implements AMs in a flexible way so that different variants can be handled dynamically. It comprises four tools: a Forecaster for use in operational forecasting, a Viewer to display the results, a Downscaler for climate studies, and an Optimizer to establish the relationship between predictands and predictors. The Forecaster handles every required processing internally, such as NWP output downloading (when possible) and reading as well as grid interpolation, without external scripts or file conversion. The processing of a forecast requires low computing efforts and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a 3-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment with several levels of synthesis and detail. This allows for the provision of a quick overview of the potential critical situations in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows for the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a semiautomatic sequential approach, Monte Carlo simulations, and a global optimization technique, using genetic algorithms. Establishing a statistical relationship between predictors and predictands is computationally intensive because it requires numerous assessments over decades. To this end, the code was highly optimized for computing efficiency, is parallelized (using multiple threads), and scales well on a Linux cluster. This procedure is only required to establish the statistical relationship, which can then be used for forecasting or downscaling at a low computing cost.

scholarly journals AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING

2019 ◽  
Author(s):  
Pascal Horton
Keyword(s):  
Climate Change ◽  
Prediction Models ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Swiss Alps ◽  
Raspberry Pi ◽  
Statistical Relationship ◽  
Impact Studies ◽  
Climate Studies

Abstract. Analog methods (AMs) allow for the prediction of local meteorological variables of interest (predictand) such as the daily precipitation, on the basis of synoptic variables (predictors). They can rely on outputs of numerical weather prediction models in the context of operational forecasting or outputs of climate models in the context of climate impact studies. AMs require low computing capacity and have demonstrated a useful potential for application in several contexts. AtmoSwing is an open source software written in C++ that implements AMs in a flexible manner so that different variants can be handled dynamically. It comprises four tools: a Forecaster that performs operational forecasts, a Viewer for displaying the results, a Downscaler for climate studies, and an Optimizer for inferring the relationship between the predictand and predictors. The Forecaster handles every required processing internally, such as operational predictor downloading (when possible) and reading, grid interpolation, etc., without external scripts or file conversion. The processing of a forecast is extremely low-intensive in terms of computing infrastructure and can even run on a Raspberry Pi computer. It provides valuable results, as revealed by a three-year-long operational forecast in the Swiss Alps. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical events in the upcoming days, as well as the possibility for the user to delve into the details of the forecasted predictand and criteria distributions. The Downscaler allows the use of AMs in a climatic context, either for climate reconstruction or for climate change impact studies. When used for future climate studies, it is necessary to pay close attention to the selected predictors, so that they contain the climate change signal. The Optimizer implements different optimization techniques, such as a sequential approach, Monte–Carlo simulation, and a global optimization technique using genetic algorithms. The process of inferring a statistical relationship between predictors and predictand is quite intensive in terms of processing because it requires numerous assessments over decades. To this end, the Optimizer was highly optimized in terms of computing efficiency, is parallelized over multiple threads and scales well on a Linux cluster. This procedure is only required to infer the statistical relationship, which can then be used in forecasting or downscaling at a low computing cost.

scholarly journals Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

2022 ◽  
Vol 2022 ◽  
pp. 1-18
Author(s):  
Dereje Tekilu Aseffa ◽  
Harish Kalla ◽  
Satyasis Mishra
Keyword(s):  
Optimization Technique ◽  
Research Work ◽  
Optimization Techniques ◽  
Magnetic Sensors ◽  
Batch Size ◽  
Raspberry Pi ◽  
Prototype Development ◽  
Embedded Platform ◽  
Artificial Intelligence Technology ◽  
Banknote Recognition

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

A new method of PWV retrieval over land with remote sensing data: a case of AMSR2

2020 ◽  
Author(s):  
Nan Jiang ◽  
Yan Xu ◽  
Tianhe Xu
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Global Climate ◽  
Weather Prediction ◽  
Remote Sensing Data ◽  
Precipitable Water ◽  
Global Scale ◽  
Brightness Temperatures ◽  
Climate Studies

<p>Precipitable water vapor (PWV) is an important parameter reflecting the amount of solid water in the atmosphere, which is widely utilized in the studies of numerical weather prediction (NWP) and climate change. The microwave radiance measurements made by the space-based remote sensing satellites give us the opportunity to make the climate studies on a global scale. So far, PWV retrieval over the ocean has a long data record and the technology is very mature, but in the case of PWV retrieval over land, it is more challenging to isolate the atmospheric signals from the varied surface signals. In this study, we will apply a new retrieval method over land based on the dual-polarized difference (vertical and horizontal) at 19 GHz and 23 GHz using the brightness temperatures from the Global Change Observation Mission-Water (GCOM-W)/Advanced Microwave Scanning Radiometer 2 (AMSR2). We found polarization difference in brightness temperatures has an exponential relation on the amount of PWV. The validation results of the PWV retrieval from the ground-based GNSS stations show that the proposed method has a mean accuracy of 3.9 mm. Thus, the proposed method can give a possibility to improve the accuracy of data assimilation in the NWP applications and is useful for the studies of global climate change with the long-term data records.</p>

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals The importance of hydrological uncertainty assessment methods in climate change impact studies

2014 ◽  
Vol 18 (8) ◽  
pp. 3301-3317 ◽  
Author(s):  
M. Honti ◽  
A. Scheidegger ◽  
C. Stamm
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Hydrological Model ◽  
Uncertainty Assessment ◽  
Assessment Method ◽  
Calibration Data ◽  
Relative Importance ◽  
Impact Studies ◽  
Uncertainty Sources ◽  
Change Impact

Abstract. Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

scholarly journals Fine-resolution climate projections enhance regional climate change impact studies

Eos ◽  
2007 ◽  
Vol 88 (47) ◽  
pp. 504-504 ◽  
Author(s):  
Edwin P. Maurer ◽  
Levi Brekke ◽  
Tom Pruitt ◽  
Philip B. Duffy
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climate Projections ◽  
Impact Studies ◽  
Change Impact ◽  
Fine Resolution

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

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