Solar Radiation Prediction Using NARX Model

The human brain, like every vital organ, is constituted of neurons. It is through this organ that we can learn and reason, reflect and memorize. The geniality of human brain and more particularly of its neurons motivates several researchers to interest to this research and to benefit from its biological aspect. The idea was to reproduce, in an artificial way, the behaviors observed in man. It was in 1943 that the first artificial neural network (ANN) was created by Warren McCulloch and Walter Pitts. It is a simple elementary processor imitating the structure and the functioning from the biological neuron. Artificial neural network is characterized by its capacity to learning and generalizing. It represents a very powerful tool. It provided multiple solutions to different complex problems. In these recent years, its effectiveness is proved in various researches fields. ANN is subdivided on two main groups, the static and dynamic neural network. The choice of the one or the other neural network type depends to the application to be processed and the complexity of model. For static neural network, information propagates in a single direction, layer by layer, and from the inlet to the outlet. They are generally used in various applications such as classifications, pattern recognition, and functions approximation. For the dynamic neural network dynamic neural network is not limited. Each neuron can send and receive information from all other neurons. The dynamic neural network architecture includes frequently one or more cycles which necessarily contain at least one delay connection. This gives rise to the dynamism notion. This neural network type is more complex than the static one, but it is more efficient for some particular applications such as dynamic modeling, monitoring, and process control. In this chapter, nonlinear autoregressive models with exogenous input (NARX) model, as type of dynamic neural network, will be used to the solar radiation prediction. Simulation results will be presented to prove the effectiveness of this model compared to those obtained using the static one

Prediction the dynamic of changes in the concentrations of main greenhouse gases by an artificial neural network type NARX

The paper considered the use of one of the most accurate artificial neural networks for predicting time series - a nonlinear autoregressive neural network with external input (NARX) for predicting the dynamics of changes in the concentrations of the main greenhouse gases. The data were obtained in the course of monitoring the dynamics of changes in the main greenhouse gases on the Arctic island Belyy, Russia. The data of the surface concentration of methane, carbon dioxide, carbon monoxide and water vapor were used. A time interval of 168 hours was chosen for the study during the summer period (July-August 2016). The NARX model accurately predicted concentration changes for all greenhouse gases. © 2020 American Institute of Physics Inc.. All rights reserved

The forecast of the methane concentration changes for the different time periods on the arctic island bely

The paper predicts the changes in the concentration of one of the main greenhouse gases - methane (CH4). The forecast was made for three different time periods, each of which had its own characteristics of the dynamics of changes in the concentration of CH4. Data for the study were collected while monitoring the content of the main greenhouse gases in the surface layer of atmospheric air in the Russian Arctic (Bely Island, Yamalo-Nenets Autonomous Okrug). We compared the results of the models prediction based on the two types of artificial neural networks: Elman and nonlinear autoregressive neural network with external input (NARX). NARX showed a high prediction accuracy for all studied time intervals. © 2020 American Institute of Physics Inc.. All rights reserved

Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model

An algorithm based on predicting the residuals of the nonlinear autoregressive neural network model with external input (NARX), which can improve the prediction accuracy, was proposed. Data of the concentration of one of the main greenhouse gases methane (CH4) on the Arctic Island of Belyy, Russia, were used for prediction. A time interval, which was characterized by high daily fluctuations in the CH4 concentration was selected. The forecast accuracy was determined by the mean absolute error (MAE), root mean squared error (RMSE) and root mean squared relative error (RMSRE) errors. The use of the algorithm allowed to increase the forecast accuracy from 11% for RMSE to 20% for RMSRE. © 2020 American Institute of Physics Inc.. All rights reserved

Traffic Occupancy Prediction Using a Nonlinear Autoregressive Exogenous Neural Network

The main aim of the intelligent transportation systems is the ability to accurately predict  traffic characteristics like traffic occupancy, speed, flow and accident based on historic and real time data collected by these systems in transportation networks. The main challenge of  a huge quantity of traffic data collected automatically, stored and processed by these systems is the way of handling and extracting the required traffic data to formulate the prediction traffic characteristic model. In this research, the required traffic data of a specified road link in UK are extracted from the big raw data of the SCOOT system by designing C++ extractor program. In addition, short term traffic prediction models are created by using deep learning technique NARX neural network to find accurate and exact traffic occupancy. Three scenarios of time interval which are 10 minutes, 20 minutes and 30 minutes are considered for analyzing the prediction accuracy. The results showed that the prediction models for the 30 minutes interval scenario have very good accuracy in estimating the future traffic occupancy compared to another scenarios of time intervals. In addition, the testing and validation study showed that the prediction models for 30 minutes intervals for particular road link yield better accuracy than 10 minutes and 20 minutes intervals

Design of a hybrid method exploiting different ınsolation states for solar radiation forecasting

Güneş enerjisinin sürekli genişlemesi, radyasyonun doğru tahminini önemli bir konu haline getirmiştir. Güneş enerjisi üretiminin doğru bir tahmini, fotovoltaik (PV) ve rüzgar jeneratörlerinin akıllı şebekelere etkin entegrasyonu için çok önemlidir. Güneş enerjisinin kesintili doğası, yenilenebilir enerji sistemi operatörleri için operasyonel planlama ve zamanlama açısından birçok zorluk teşkil etmektedir. Bu nedenle güneş ışınımının hibrit yöntemlerle tahmin edilmesi yaygınlaşmaktadır. Bu yazıda, güneş radyasyonunu tahmin etmek için bir hibrit yöntem önerilmiş olup, burada tahmin modeli açıklık indeksine dayalı olarak belirlenir. Çalışmada, Mardin ilinin Türkiye Meteoroloji Genel Müdürlüğünden (TMGM) elde edilen iki yıllık güneş radyasyonu verileri kullanılmıştır. Tahmin edici olarak YSA, NARX ağları ve Ridge regresyon yöntemleri kullanılmış ve çalışmanın ilk aşamasında eğitim verileri her üç yaklaşımla da modellenmiştir. Bulutluluk indeksi için, az bulutlu, bulutlu ve çok bulutluya karşılık gelecek şekilde üç aralık belirlenmiştir. Tahmin edici olarak kullanılan üç yöntem ile eğitim verisi modellenmiş ve her bir yöntemin belirlenen her bir bulutluluk indeksi aralığındaki başarısı incelenmiştir. Sonuç olarak, hibrit tahmin algoritmasında, önce yapay sinir ağları kullanılarak açıklık indeksi tahmin edilir ve daha sonra tahmin edilen açıklık indeksi aralığında en başarılı model kullanılarak gelecekteki güneş radyasyonu değeri tahmin edilir. Deneysel sonuçlar, önerilen hibrit yöntem ile modellerin bireysel olarak kullanıldığı duruma göre daha başarılı tahminler yapıldığını göstermektedir.The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually

Método para la Predicción de Demanda Mensual de Electricidad en Colombia utilizando Análisis Wavelet y Modelos Auto-regresivos No Lineales

This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN) of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT); a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR) model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing.A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in predictionEn este artículo se propone un método para la predicción mensual de la demanda en el Sistema Interconectado Nacional Eléctrico de Colombia. El método realiza preprocesamiento de la serie de tiempo utilizando un análisis multiresolución mediante tranformada wavelet discreta; se presenta un estudio para la selección de la wavelet madre y su orden, asi como del nivel de descomposición. Dado que originalmente la serie tiene comportamiento no lineal, se utilizó igualmente un modelo no lineal autoregresivo. La predicción se obtiene añadiendo a la tendencia, el estimado obtenido con el residual de la serie combinado con otros componentes extraídos durante el preproceamiento.Se incluye una revisión bibliográfica de investigaciones realizadas internacionalmente y en Colombia en relación a la aplicación de la transformada wavelet y el modelo autoregresivo no lineal a la predicción de energía eléctrica

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance