Wyniki wyszukiwania - BazTech

1

Forecasting the required quantity of cement manufacturing materials using time series and Q-Network techniques

Madjour Hassina, Zermane Hanane, Benaicha Sonia

Ecological Chemistry and Engineering. S

|

2025

|

Vol. 32, nr 3

415--426

EN

In the era of Industry 4.0, accurate prediction of industrial process parameters is essential for optimising operations, lowering costs, and enhancing product quality. Traditional statistical methods often struggle to capture the complex temporal dependencies within industrial processes. This study explores the use of Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Q-Network models to predict material quantities in an industrial dataset. The dataset was pre-processed to address missing values and outliers, and the models were evaluated based on Mean Squared Error (MSE), R2, and accuracy. The results show that the LSTM model achieved an MSE of 14.253 and an R2 of 0.700. The BiLSTM model greatly outperformed it, with an MSE of 0.714 and an R2 of 0.985. The Q-Network model produced an MSE of 0.005 and an R2 of 0.992. These findings demonstrate the Q-Network’s superior ability to capture temporal dependencies within the data.

2

Multi-scale neural networks classification of mild cognitive impairment using functional near-infrared spectroscopy

Kang Min-Kyoung, Hong Keum-Shik, Yang Dalin, Kim Ho Kyung

Biocybernetics and Biomedical Engineering

|

2025

|

Vol. 45, iss. 1

11--22

EN

Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: N-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the N-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.

3

Machine learning methods for time series prediction of the diffusion curve

Zaręba Mateusz, Skiba Marta

Archives of Mining Sciences

|

2025

|

Vol. 70, no. 4

561--574

EN

This study employed machine learning techniques to predict time series of diffusion curves, generated in Python with NumPy library. The data was structured as a time series to enable efficient model training and evaluation. Various approaches – statistical, neural, and regression-based – were tested to model the diffusion dynamics. Results revealed notable performance differences: regression models achieved the highest accuracy with the lowest error rates, while time series models like ARIMA and TCN performed worse, likely due to difficulties in capturing the process’s complexity.

4

Time series analysis of hazardous events based on data recorded in a polish construction company

Woźniak Zuzanna, Nowobilski Tomasz, Hoła Bożena

Archives of Civil Engineering

|

2025

|

Vol. 71, nr 3

295--309

EN

The construction sector records a significant number of occupational accidents (A) and near-misses (NM), making it one of the most dangerous in the economy. In recent years, interest in near-miss events has been growing among researchers and practicing engineers, as they are considered precursors to occupational accidents. Based on a review of the literature on the subject and their own experience, the authors of the article conclude that there is a significant gap in research on near misses in the Polish construction industry. The authors believe that such studies are necessary in the context of accident reduction. The purpose of this article is to analyze the time series of near misses and accidents at work. The data used in the study come from the system of registration of hazardous events implemented in one of the Polish construction companies, recorded in 2015-2022. Due to the specific nature of construction work and the circumstances of the event, 8 categories of hazardous events were specified. For each category, a time series was built to inform about the dynamics of the changes taking place. Box plots were developed for random variables representing the time intervals between consecutive events, informing about the statistical characteristics of a given set of events (SHEi ). This research makes it possible to predict the occurrence of specific events over time and to introduce preventive measures in construction practice.

PL

W sektorze budowlanym notuje się znaczną liczbę wypadków przy pracy (A) oraz zdarzeń potencjalnie wypadkowych (NM), co sprawia, że jest on uznawany za jeden z najbardziej niebezpiecznych w gospodarce. W ostatnich latach zainteresowanie zdarzeniami potencjalnie wypadkowymi wśród naukowców i inżynierów praktyków wzrasta, gdyż są one uważane za prekursory wypadków przy pracy. Na podstawie przeprowadzonego przeglądu literatury przedmiotu oraz doświadczeń własnych, autorzy artykułu stwierdzają, że istnieje istotna luka w badaniach dotyczących zdarzeń potencjalnie wypadkowych w polskim budownictwie. Autorzy uważają, że takie badania są niezbędne w kontekście redukcji liczby wypadków. Celem niniejszego artykułu jest analiza szeregów czasowych zdarzeń potencjalnie wypadkowych i wypadków przy pracy. Dane wykorzystane w badaniach pochodzą z systemu rejestracji zdarzeń niebezpiecznych zaimplementowanego w jednej z polskich firm budowlanych, zarejestrowanych w latach 2015-2022. Ze względu na specyfikę robót budowlanych i okoliczności zdarzenia, wyszczególniono 8 kategorii zdarzeń niebezpiecznych. Dla każdej kategorii zbudowano szereg czasowy informujący o dynamice zachodzących zmian. Opracowano wykresy skrzynkowe, dla zmiennych losowych odstępu czasu między kolejnymi zdarzeniami, informujące o charakterystykach statystycznych danego zbioru zdarzeń (SHEi ). Badania te pozwalają na prognozowanie wystąpienia określonych zdarzeń w czasie oraz wprowadzanie środków zapobiegawczych w praktyce budowlanej. Zbiór wszystkich zdarzeń niebezpiecznych (SHE) został podzielony na zbiór zdarzeń potencjalnie wypadkowych (SNM) i zbiór wypadków (SA). Każdy z tych zbiorów został poddany kategoryzacji ze względu na bezpośrednią przyczynę zdarzenia. Podzbiory zostały podzielone na 8 kategorii: SHE1 – uderzenie przedmiotami, SHE2 – najechanie / potrącenie, SHE3 – środowisko pracy, SHE4 – upadek człowieka, SHE5 – elektryczność, SHE6 – pożar / wybuch / odnalezienie niewybuchu, SHE7 – zawalenie / przysypanie / uwięzienie, SHE8 – kontakt z ruchomymi elementami maszyn. Wprowadzono nową zmienną reprezentującą czas między kolejnymi zdarzeniami. Zmienna ta określa liczbę dni pomiędzy datami następujących po sobie zdarzeń w sekwencji. Procedura wyznaczania odstępów czasowych została wykonana dla ośmiu kategorii zdarzeń, w odniesieniu do wypadków przy pracy jak i zdarzeń potencjalnie wypadkowych. Opracowano wykresy skrzynkowe dla zestawów zmiennych losowych utworzonych w każdej kategorii zdarzeń niebezpiecznych. Prezentują one kluczowe statystyki opisujące zmienne, takie jak mediany, odchylenia od nich, wartości średnie, wartości odstające oraz ekstremalne.

5

Dependence of seasonal phenomena in coordinate time series on GNSS antenna mounting height

Maciuk Kamil, Olejarz Grzegorz, Abdelhamid Mohamed, Bem Agata, Krawczyk Karol, Postek Paweł, Maciejewska Aleksandra

Advances in Geodesy and Geoinformation

|

2025

|

Vol. 74, no. 1

art. no. e60, 2025

EN

The purpose of this paper is analysing the correlation between the magnitude of the annual amplitude of seasonal changes in the coordinate components of GNSS reference stations and the height of the antenna mounting above the ground. For this purpose, the daily coordinate solutions of more than 500 GNSS reference stations that are part of the IGS (International GNSS Service) network were studied due to their distribution across the globe and long operating time, for some stations dating back to the 1990s. To minimize the impact of the tectonic plate movements authors adopted coordinates of reference stations inside each of the 21 tectonic plates. The coordinates in a topocentric reference frame were detrended in accordance with a linear model, with the objective of removing first-order trends. Subsequently, the seasonal yearly functions were calculated for each North, East and Up component. Finally, the amplitude of the seasonal factor for each station was determined. As a result of the analysis, the existence of annual amplitudes of coordinate changes was demonstrated for some of the stations, but no significant correlation between this phenomenon and the height of the GNSS antenna mounting was shown. In the case of the horizontal components, the majority of the station’s time series is characterized by the amplitude of seasonal function does not exceed 2.5–3 mm, and 5 mm for the vertical component.

6

Simulation analysis of artificial neural network and XGBoost algorithms in time series forecasting

Sroka Łukasz

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

|

2024

|

z. 195

561--575

EN

Purpose: The aim of the article was to prepare a simulation analysis of artificial neural network and XGBoost algorithm with determining which of the method was characterized by a lower level of forecast errors for time series predictions. Design/methodology/approach: The objective of the article was reached by applying, a simulation study on a sample of 1000 artificially generated time series. The analyzed XGBoost algorithm and the artificial neural network ANN model were intended to prepare forecasts for five periods ahead. These forecasts were compared with the actual implementations of the time series and proposed forecast error measures. Findings: It is possible to use simulated time series to check which of the presented algorithms were characterized by a lower forecast error. The study showed that applying of the artificial neural networks ANN to forecast future observations generated a lower level of MAPE, MAE and RMSE errors than in the case of the XGBoost algorithm. It was found that both methods generate a lower level of forecast error for time series characterized by a high level of mean value, standard deviation and variance, and levels of kurtosis and skewness close to 0. Practical implications: The research results can be used by both investors and enterprises to better adjust their business decisions to changing market prices by using a model with a lower forecast bias. Originality/value: The original contribution of this article is a comprehensive comparison of forecasts generated by the XGBoost and ANN algorithm, along with determining for which types of time series of the algorithms forecast future values with less error. Moreover, due to the use of simulated artificial time series, it was possible to test each algorithm for various market conditions.

7

Analiza wybranych aspektów pracy KSE pod kątem zapotrzebowania mocy

Malska Wiesława

Przegląd Elektrotechniczny

|

2024

|

R. 100, nr 7

121--126

PL

W artykule zaprezentowano analizę wybranych aspektów pracy Krajowego Systemu Elektroenergetycznego pod kątem zapotrzebowania mocy. Przedstawiono wyniki analiz i obliczeń z wykorzystaniem programu komputerowego Statistica dla dobowej prognozy zapotrzebowania mocy i rzeczywistego zapotrzebowania mocy, a także zaprezentowano model wyrównywania wykładniczego i predykcji.

EN

The article presents an analysis of selected aspects of the operation of the National Power System in terms of power demand. The results of analysis and calculations using the Statistica computer software for daily power demand forecast and actual power demand are presented, and an exponential equalization and forecasting model is presented.

8

Optimizing time series forecasting: leveraging machine learning models for enhanced predictive accuracy

Wójcik Waldemar, Shayakhmetova Assem, Akhmetova Ardak, Abdildayeva Assel, Galymzhan Nurtugan

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2024

|

T. 14, nr 4

115--120

EN

Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resiliencein the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained successof an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parametersin business, including investments and their impact on the financial health of the company. In this research, explored a variety of time series modelsand identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE valuesof 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretabilityof the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable developmentand prosperity of the enterprise in the long term.

PL

Zaangażowanie w działalność inwestycyjną odgrywa kluczową i strategiczną rolę we wspieraniu rozwojuprzedsiębiorstw i zapewnianiuich stabilności na rynku. Zaangażowanie to pociąga za sobą wydatki na nabycie aktywów, wdrażanie postępu technologicznego, zwiększanie zdolności produkcyjnych, prowadzenie badań i rozwoju oraz wiele innych obszarów. Łącznie aspekty te stanowią podstawę trwałego sukcesu organizacjiw perspektywie długoterminowej. Niniejsza rozprawa dotyczy wykorzystania technik uczenia maszynowego do prognozowania kluczowych parametróww biznesie, w tym inwestycji i ich wpływu na kondycję finansową firmy. W tym artykule zbadano różne modele szeregów czasowych i stwierdzono,że zarówno modele Random Forest Regressor, jak i Decision Tree Regressor zapewniają najwyższą dokładność, wykazując identyczne wartości RMSE wynoszące 88,36 w zbiorze danych walidacyjnych. Co więcej, modele Cat Boost i Light GBM wykazały się godną pochwały wydajnością, rejestrując wartości RMSE odpowiednio 92,47 i 104,69. Wyniki te podkreślają solidną wydajność regresorów Random Forest Regressor i Decision Tree Regressor, podkreślając ich zdolność do dostarczania dokładnych prognoz. Należy zauważyć, że Random Forest Regressor i Decision Tree Regressor wyróżniająsię wysoką dokładnością w prognozowaniu szeregów czasowych, a wybór między nimi powinien uwzględniać kompromisy między wydajnością obliczeniową a interpretowalnością modelu. Wyniki te pozwalają nam zaproponować praktyczne strategie zarządzania zasobami inwestycyjnymiw celu zapewnienia zrównoważonego rozwoju i dobrobytu przedsiębiorstwa w perspektywie długoterminowej.

9

Analiza statystyczna danych oraz prognozy rynkowych cen energii (RCE) w Polsce z wyprzedzeniem do 1 godziny

Piotrowski Paweł, Baranowski Marcin, Bączek Adrian, Niedźwiecki Radosław

Elektro Info

|

2023

|

nr 4

81--85

PL

W artykule przedstawiono analizę statystyczną danych oraz prognozy rynkowych cen energii (RCE) z wyprzedzeniem do 1 godziny. Sformułowano wnioski końcowe z wykonanych prognoz oraz analiz statystycznych.

EN

The article presents a statistical analysis of data and forecasts of energy prices (RCE) in Poland up to 1 hour ahead. The conclusions have been drawn based on forecasts outcome and statistical analysis.

10

Dwa nowe algorytmy klasyfikacji farmakokinetycznych szeregów czasowych

Wilczek Piotr

Biuletyn Naukowy Wrocławskiej Wyższej Szkoły Informatyki Stosowanej. Informatyka

|

2023

|

Vol. 10

27--43

PL

Przedkładana praca zawiera propozycję dwóch nowych miar odległości pomiędzy szeregami czasowymi. Mianowicie, wprowadzamy tzw. rekurencyjno-kanoniczną miarę odległości oraz jej krzyżową formę. Nasze podejście opieramy na bezprogowych (krzyżowych) macierzach rekurencyjnych, których koncepcje zostały rozwinięte w ramach (krzyżowej) analizy rekurencyjnej szeregów czasowych oraz na kanonicznej mierze odległości pomiędzy dwoma wielowymiarowymi zbiorami danych, której koncepcja została rozwinięta w obrębie chemometrii. Wyczerpujące symulacje komputerowe przeprowadzone na 27 farmakokinetycznych szeregach czasowych pokazały, iż algorytmy bazujące na nowo opracowanych funkcjach odległości są bardziej efektywne niż algorytmy bazujące na klasycznych miarach 𝐿2 i 𝐷𝑇𝑊 oraz na ich modyfikacjach.

EN

In the present contribution, we proposed two novel distance measures between time series. Namely, we introduced the so-called recurrence-canonical measure of distance as well as its cross form. Our approach is based on the notion of the unthresholded (cross-)recurrence matrices developed in the field of the (Cross-)Recurrence Quantification Analysis and the notion of the canonical measure of distance between multidimensional datasets developed in the field of chemometrics. The extensive computer simulations carried out on 27 pharmacokinetic time series showcased that the algorithms based on the newly designed distance functions outperform the protocols based on the classical 𝐿2 and 𝐷𝑇𝑊 functions and on their modifications.

11

Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning

Jayagopal Prabhu, Muthukumaran Venkatesan, Koti Manjula Sanjay, Kumar Sampath Satheesh, Rajendran Sukumar, Mathivanan Sandeep Kumar

Acta Geophysica

|

2022

|

Vol. 70, no 6

2901--2916

EN

Crop yield is completely vulnerable to extreme weather events. Growing research investigation to establish climate change, implications in the sectors are influencing the connection. Forecasting maize output with some lead time can help producers to prepare for requirement and, in many cases, limited human resources, as well as support in strategic business decisions. The major purpose is to illustrate the relationship between various climatic characteristics and maize production, as well as to predict forecasts using ARIMA and machine learning approaches. When compared to ARIMA, the proposed method performs better in forecasting maize yields. Consequently, the neural network provides the majority of the prospective talents for forecasting maize production. Seasonal growth is susceptible of forecasting crop yields with tolerable competencies, and efforts are essential to quantify the proposed methodology that forecasts overall crop yield in diverse neighbourhoods in Saudi Arabia’s regions. The proposed combined ARIMA-LSTM model requires less training, with parameter adjustment having less effect on data prediction without bias. To monitor progress, the model may be trained repeatedly using roll back. The correlations between estimated yield and measured yield at irrigation and rain-fed sites were analysed to further validate the robustness of the optimal ARIMA-LSTM method, and the results demonstrated that the proposed model can serve as an effective approach for different types of sampling sites and has better adaptability to inter-annual fluctuations in climate with findings indicating a dependable and viable method for enhancing yield estimates.

12

Sztuczna inteligencja – klucz do rozwoju sieci 5G

Koper Zbigniew

Wiadomości Elektrotechniczne

|

2021

|

R. 89, nr 11

13--16

PL

Rozwój sieci 5G jest niemożliwy bez zastosowania sztucznej inteligencji (AI). Zastosowania AI obejmuje planowanie sieci, diagnostykę sieci oraz jej optymalizację i kontrolę. W artykule omówiono wybrane zagadnienia związane z wykorzystaniem ML, koncentrując się na szeregach czasowych i ich wykorzystaniu do przewidywania stanu sieci.

13

Matrix profile for DDoS attacks detection

Alotaibi Faisal, Lisitsa Alexei

Annals of Computer Science and Information Systems

|

2021

|

Vol. 25

357--361

EN

Previous studies have focused on DDoS, which are a crucial problem in network security. This study explore a time series method MP, which has shown effective results in a number of applications. The MP is potentially well suited to use for DDoS as a rapid method of detection,a factor that is vital for the successful identification and cessation of DDoS.The study examined how the MP performed in diverse situations related to DDoS, as well as identifying those features that are most applicable in various scenarios.Results show the efficiency of MP against all types of DDoS with the exception of NTP.

14

Impact of time series clustering on fuel sales prediction results

Henzel Joanna, Sikora Marek, Bularz Jakub

Annals of Computer Science and Information Systems

|

2021

|

Vol. 26

13--21

EN

The purpose of the paper is to check the impact of data clustering in the process of predicting demand. We checked different ways of adding information about similar datasets to the forecasting process and we grouped the measurements in multiple ways. The experiments were executed on 50 time series describing fuels sales (gasoline and diesel sales) on 25 petrol stations from an international company. We described the data preparation process and feature extraction process. In the 9 presented experiments, we used the XGBoost algorithm and some typical time series forecasting methods (ARIMA, moving average). We showed a case study for two datasets and we discussed the practical usage of the tested solutions. The results showed that the solution which used XGBoost model utilising data gathered from all available petrol stations, in general, worked the best and it outperformed more advanced approaches as well as typical time series methods.

15

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Sridhar Sashank, Sanagavarapu Sowmya

Annals of Computer Science and Information Systems

|

2021

|

Vol. 25

147--150

EN

Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92%.

16

Innovative trend pivot analysis method (ITPAM): a case study for precipitation data of Susurluk Basin in Turkey

Ceribasi Gokmen, Ceyhunlu Ahmet Iyad, Ahmed Naveed

Acta Geophysica

|

2021

|

Vol. 69, no. 4

1465--1480

EN

Throughout the geological history of the earth, there have been many climate changes due to natural and external factors. In the past, the changes in climate were caused by natural causes, and today it is primarily caused by human activities. Besides being diferent climate types, Turkey is among countries that will be afected by climate change induced by global warming. Climate changes in the regions will be afected diferently and degrees due to the country’s surroundings by seas, fragmented topography and orographic features. Trend analysis methods are used in many areas such as on various engi neering, agriculture, environmental and water resources, especially in climate change impact studies resulting from global warming. When data are analyzed with classical trend analysis methods, forward-looking predictions are generally made as low, medium, high, decreasing and increasing. However, risk classes showing changes between available data sets are not known. Innovative Trend Pivot Analysis Method (ITPAM) determines risk classes by establishing a relationship between data. Furthermore, in this method, increasing and decreasing trend regions are separated into fve classes more clearly than classical/traditional trend methods. In this study, Susurluk Basin’s total monthly precipitation data (2006–2017) were analyzed by using ITPAM which the newest trend method. When arithmetic mean analysis results are examined, a signifcant change is observed between frst data set and second data set at two stations (Bandirma and Uludag). When examined at other stations, it is observed that at least one month of almost every station is in 1st degree risk group. When standard deviation analysis results of each station are examined, a signifcant change is observed between frst data set and second data set at many stations. Because while trend class of a point in developed IPTA graph is the medium degree, this point is in 1st risk class in the risk graph.

17

Research on the combustion process using time series

Grądz Żaklin

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2020

|

T. 10, nr 2

52--55

EN

In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.

PL

W procesie spalania jednym z najważniejszych zadań jest zachowanie jego stabilności. Do tego celu wykorzystywanych jest wiele metod z zakresu monitorowania, diagnostyki i analizy danych pomiarowych. Zarejestrowane w komorze spalania informacje są jednowymiarowymi szeregami czasowymi. W przypadku niestacjonarnych szeregów czasowych, które można przekształcić do formy stacjonarnej, znalazły zastosowanie scałkowane procesy autoregresji i średniej ruchomej. W artykule przedstawiono problematykę prognozowania zmian intensywności świecenia płomienia. Badania zaprezentowane w pracy zostały przeprowadzone z wykorzystaniem modelu ARIMA(p,d,q). Przedstawione prognozy zmian intensywność świecenia płomienia odwzorowują rzeczywiste przebiegi, co pozwala wykorzystać je w diagnostyce i sterowaniu procesem spalania.

18

Analiza rozwoju elektromobilności w Polsce oraz prognozy liczby pojazdów z napędem elektrycznym do roku 2025

Piotrowski Paweł

Elektro Info

|

2020

|

nr 5

74--77

PL

Analiza rozwoju elektromobilności w Polsce oraz prognozy liczby pojazdów z napędem elektrycznym do roku 2025

EN

The analysis of e-mobility development in Poland and forecasts of the number electric vehicles by 2025

19

Adaptation of models from determined chaos theory to short-term power forecasts for wind farms

Popławski T., Szeląg P., Bartnik R.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2020

|

Vol. 68, nr 6

1491--1501

EN

The paper proposes an adaptation of mathematical models derived from the theory of deterministic chaos to short-term power forecasts of wind turbines. The operation of wind power plants and the generated power depend mainly on the wind speed at a given location. It is a stochastic process dependent on many factors and very difficult to predict. Classical forecasting models are often unable to find the existing relationships between the factors influencing wind power output. Therefore, we decided to refer to fractal geometry. Two models based on self-similar processes (M-CO) and (M-COP) and the (M-HUR) model were built. The accuracy of these models was compared with other short-term forecasting models. The modified model of power curve adjusted to local conditions (M-PC) and Canonical Distribution of the Vector of Random Variables Model (CDVRM). Examples of applications confirm the valuable properties of the proposed approaches.

20

Water quality management in a treatment plant using the Box-Jenkins method

Redondo-Peñuela E. A., Rondón-Quintana H. A., Zafra-Mejía C. A.

Archives of Civil Engineering

|

2020

|

Vol. 66, nr 3

125--137

EN

Drinking water systems are critical to society. They protect residents from waterborne illnesses and encourage economic success of businesses by providing consistent water supplies to industries and supporting a healthy work force. This paper shows a study on water quality management in a treatment plant (TP) using the Box-Jenkins method. A comparative analysis was carried out between concentrations of water quality parameters, and Colombian legislation and guidelines established by the World Health Organization. We also studied the rainfall influence in relation to variations in water quality supplied by the TP. A correlation analysis between water quality parameters was carried out to identify management parameters during the TP operation. Results showed the usefulness of the Box-Jenkins method for analyzing the TP operation from a weekly timescale (mediumterm), and not from a daily timescale (short-term). This was probably due to significant daily variations in the management parameters of water quality in the TP. The application of a weekly moving average transformation to the daily time series of water quality parameter concentrations significantly decreased the mean absolute percentage error in the forecasts of Box-Jenkins models developed. Box-Jenkins analysis suggested an influence of the water quality parameter concentrations observed in the TP during previous weeks (between 2-3 weeks). This study was probably constituted as a medium-term planning tool in relation to atypical events or contingencies observed during the TP operation. Finally, the findings in this study will be useful for companies or designers of drinking water treatment systems to take operational decisions within the public health framework.