NEURAL NETWORK CALCULATION OF CARBON DIOXIDE CONCENTRATIONS

Снижение углеродного следа в настоящее время является одной из приоритетных задач мировой экономики. Для достижения этой цели необходимо с одной стороны снижать выбросы парниковых газов, с другой стороны развивать методы мониторинга парниковых газов в атмосферном воздухе для обеспечения контроля эффективности принимаемых решений.Учитывая сложность процессов рассеивания газов в атмосферном воздухе, значительными преимуществами в вопросах определения концентраций атмосферных примесей обладают нейросетевые методы моделирования. В данной статье представлен метод расчета концентраций углекислого газа в атмосферном воздухе с помощью спроектированной и обученной каскадной нейросетевой модели, позволяющей при расчете концентраций учитывать сложное влияние метеорологических факторов и локальных условий рассеивания. Первым уровнем модели является расчет концентрации оксида углерода по известным параметрам источников выбросов этого вещества с использованием регламентированной методики расчета рассеивания примесей в атмосфере в Унифицированной программе расчета рассеивания «Эколог-Город». Вторым уровнем является нейронная сеть, которая корректирует рассчитанную на первом шаге концентрацию по заданным метеорологическим параметрам для увеличения точности моделирования. Третьим уровнем является нейронная сеть, позволяющая по полученной на предыдущем шаге концентрации оксида углерода, а также измеренным значениям коэффициента химической трансформации и концентрации атмосферного озона производить расчет концентрации углекислого газа.Полученная каскадная модель апробирована на территории г. Нижнекамск. Достигнутая точность расчета концентрации углекислого составила более 95%. Таким образом, представленная технология позволяет расширить возможности локальной системы мониторинга в условиях недостаточного количества измерений диоксида углерода. Reducing the carbon footprint is currently one of the priorities for the world economy. To do this, it is necessary to reduce greenhouse gas emissions, as well as to develop methods for monitoring greenhouse gases in the atmospheric air to ensure control over the effectiveness of decisions taken.Considering the complexity of the processes of dispersion of gases in the atmospheric air, neural network modeling methods have significant advantages in determining the concentrations of atmospheric impurities. This article presents a method for calculating the concentration of carbon dioxide in the atmospheric air using a designed and trained cascade neural network model, which makes it possible to take into account the complex influence of meteorological factors and local dispersion conditions when calculating concentrations. The first level of the model is the calculation of the concentration of carbon monoxide according to the known parameters of the emission sources of this substance using the regulated method for calculating the dispersion of impurities in the atmosphere in the Unified program for calculating dispersion "Ecolog-City". The second level is a neural network, which corrects the concentration calculated at the first step according to the specified meteorological parameters to increase the modeling accuracy. The third level is a neural network that allows calculating the concentration of carbon dioxide based on the concentration of carbon monoxide obtained at the previous step, as well as the measured values of the coefficient of chemical transformation and concentration of atmospheric ozone.The resulting cascade model was tested on the territory of Nizhnekamsk. The achieved accuracy of calculating the concentration of carbon dioxide was more than 95%. Thus, the presented technology makes it possible to expand the capabilities of the local monitoring system in conditions of an insufficient number of measurements of carbon dioxide.

Evolving Hybrid Cascade Neural Network Genetic Algorithm Space–Time Forecasting

Design: At the heart of time series forecasting, if nonlinear and nonstationary data are analyzed using traditional time series, the results will be biased. At the same time, if just using machine learning without any consideration given to input from traditional time series, not much information can be obtained from the results because the machine learning model is a black box. Purpose: In order to better study time series forecasting, we extend the combination of traditional time series and machine learning and propose a hybrid cascade neural network considering a metaheuristic optimization genetic algorithm in space–time forecasting. Finding: To further show the utility of the cascade neural network genetic algorithm, we use various scenarios for training and testing while also extending simulations by considering the activation functions SoftMax, radbas, logsig, and tribas on space–time forecasting of pollution data. During the simulation, we perform numerical metric evaluations using the root-mean-square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) to demonstrate that our models provide high accuracy and speed up time-lapse computing.

An Improved Method for Extracting Photovoltaic Module I–V Characteristic Curve Using Hybrid Learning Machine System

This paper provides an improved method for predicting the I–V curve of the photovoltaic module using a hybrid machine learning system. The proposed method is based on a random forest algorithm and a cascade forward neural network. A random forest algorithm is used to predict a specific factor that is subsequently used as an input for the cascade neural network to remove the correlation between voltage and current. Then, the actual current is predicted using the cascade neural network. This procedure assures the ability of the proposed model to extract the I–V curve of any photovoltaic module regardless of its rating or type. A dataset that contains values for air temperature, solar radiation, voltage, and current of two polycrystalline photovoltaic modules is used in the training process of the proposed algorithm. The hybrid model has general inputs such as ambient temperature, solar radiation, and data from the photovoltaic module datasheet (Voc and Isc). The proposed model is trained, tested, and validated by 86% of the data. Meanwhile, 14% of the data are used for testing. Thus, the proposed model is tested using unknown data so as to avoid overfitting. Results show that the proposed model is very accurate in predicting I–V curves based on three types of errors which are mean absolute percentage error (0.68%), mean bias error (0.0191 A), and root-mean-squared error (0.04458 A). This hybrid model can be used to obtain the I–V curves for several types of photovoltaic modules.

Statistical Downscaling of Temperature Using Global Climate Model Outputs - Effect of Bias correction

<p>General Circulation Models (GCMs) are the primary source of knowledge for constructing climate scenarios and provide the basis for quantifying the climate change impacts at multi-scales and from local to global. However, the climate model simulations have a lower resolution than the desired watershed or hydrologic scale. Different downscaling methodologies are adopted to transform the global scale (coarser resolution) climate information to the local scale (finer resolution). One of the drawbacks of the GCM simulations is the systematic bias relative to historical observations. Bias correction is thus required to adjust the simulated values to reflect the observed distribution and statistics.<strong> </strong>In this study, the effect of bias correction is evaluated on the statistical downscaling models' performance to predict the temperature. Three statistical downscaling models are used: (i) Multi-linear Regression (MLR); (ii) Generalized Regression Neural Network (GRNN); and (iii) Cascade Neural Network (CasNN). The average daily temperature simulations generated by 25 GCMs of Coupled Model Intercomparison Project Phase-5 (CMIP5) are used in the study. The analysis is carried out at 22 stations of the Upper Thames River Basin (UTRB) in Canada during the baseline period of 1950 to 2005. The downscaling models' performance is evaluated using the Pearson Correlation Coefficient (CC) and Nash Sutcliffe Efficiency (NSE). The results indicated that bias correction had improved all the downscaling models' performance at all stations of UTRB. The respective increase in CC and NSE values for (i) MLR is 8% and 10%; (ii) GRNN is 4% and 7%; and (iii) CasNN is 4% and 8%. Among the three downscaling models, multi-linear regression and cascade neural network models have shown similar performance.</p>

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.