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Optimal Prediction of Air Quality Index in Metropolitan Cities Using Fuzzy Time Series with Deep Learning Approach

Unnikrishnan Asha, Rajeswari Subramanian

Ecological Engineering & Environmental Technology

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2024

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Vol. 25, iss. 1

341--359

EN

Predicting the air quality index (AQI) with high accuracy is just as crucial as predicting the weather. The research selected a few potential meteorological parameters and historical data after taking into account a variety of complex factors to accurately anticipate AQI. The dataset was gathered, pre-processed to substitute missing values (MV) and eliminate redundant information, and before being applied to predict the AQI. The data was collected from 2019 to 2022 to analyse the AQI founded on time series forecasting (TSF). Many AQI parameters, including accumulated precipitation, the daily normal temperature, and prevailing winds, are lacking in this study. To preserve the characteristics of the time series, kNN classification was implemented to fill in the MV and integrate Principal Component Analysis (PCA) to decrease the noise of data to recover the accuracy of AQI prediction. However, the majority of research is limited due to a lack of panel data, which means that characteristics such as seasonal behaviour cannot be taken into account. Consequently, the research introduced a TSF based on seasonal autoregressive integrated moving average (SARIMA) and stochastic fuzzy time series (SFTS). The stacked dilated convolution technique (SDCT) which effectively extracts the time autocorrelation, while the time attention module focuses on the time intervals that were significantly linked with each instant. To control the strongly connected features in the data set, the Spearman rank correlation coefficient (SRCC) was utilised. The selected features included SO2, CO and O3, NO2, PM10 and PM2.5, temperature, pressure, humidity, wind speed and weather, as well as rainfall. Additionally, to estimate the AQI and SO2, PM10, PM2.5, NO2, CO, and O3 concentration from 2019 to 2022, the data of climatological elements after PCA and historical AQI were input into the multiple linear regression (MLR) techniques with a temporal convolution network (TCN) built deep learning model (DLM). The proposed DLM springs a correct and detailed assessment for AQI prediction. The experimental results confirm that the expected background yields a stable forecasting result, that the pollutant concentration of the surrounding areas affects the AQI of a place, and that the planned model outperforms existing state-of-the-art models in terms of prediction of consequences. Consequently, utilising this presented innovative approach integrates fuzzy time series with deep learning, addressing missing values and noise reduction, incorporating seasonal behaviour, utilising the SRCC for feature control, employing a comprehensive set of meteorological parameters, and presenting a hybrid model that outperforms existing models. These aspects collectively contribute to the advancement of air quality prediction methodologies, particularly in metropolitan cities. However, this hybrid approach leverages the strengths of both traditional statistical methods and deep learning techniques, resulting in a robust and accurate assessment for AQI prediction as well as providing more stable and accurate forecasting results.

2

SARIMA and Holt-Winters method based microgrids for load and generation forecasting

Shah Aftab Ahmad, Khan Zafar A., Altamimi Abdullah

Przegląd Elektrotechniczny

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2021

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R. 97, nr 12

38--44

EN

The modern power grid faces challenges regarding many complex factors affecting both demand and generation; including growth in demand; incorporating large-scale renewable power penetration; uncertainties in climate change; lack of historical data; and coordination of the large volume of data. These issues have resulted in complications in forecasting load and generation in microgrids. The loads are becoming more erratic and the generation is intermittent. Thus, this paper presents a study of different forecasting approaches for load and generation, by comparing multiple univariate and multivariate methods to analyse their effect. The study also proposes seasonal models: the SARIMA model taking into consideration the historical load, the correlation of weather data and renewable integration to estimate future behaviour of the microgrid by predicting one day ahead using critical load data; whereas the Holt Winters method is used for generation forecasting. A case study is simulated using realworld microgrid data for the selected geographic location in Australia. The results suggest that for the day-ahead load forecast, the SARIMA model performed relatively better compared to MLR, Holt-Winters additive and multiplicative methods; whereas, for generation forecasting, Holt-Winters Additive Method and SARIMA perform well for Autumn and Summer respectively. The results suggest that the proposed approach of using different seasonal models for load and generation forecasting yields higher accuracy as compared to conventional forecasting.

PL

Nowoczesna sieć energetyczna stoi przed wyzwaniami dotyczącymi wielu złożonych czynników wpływających zarówno na popyt, jak i na wytwarzanie; w tym wzrost popytu; włączenie penetracji energii odnawialnej na dużą skalę; niepewność w zmianach klimatu; brak danych historycznych; i koordynacja dużej ilości danych. Problemy te spowodowały komplikacje w prognozowaniu obciążenia i generacji w mikrosieciach. Obciążenia stają się coraz bardziej nieregularne, a generacja jest przerywana. Dlatego w niniejszym artykule przedstawiono badanie różnych podejść do prognozowania obciążenia i generacji, porównując wiele metod jednowymiarowych i wielowymiarowych w celu przeanalizowania ich wpływu. W badaniu zaproponowano również modele sezonowe: model SARIMA uwzględniający obciążenie historyczne, korelację danych pogodowych i integrację odnawialną w celu oszacowania przyszłego zachowania mikrosieci poprzez prognozowanie z jednodniowym wyprzedzeniem przy użyciu danych o obciążeniu krytycznym; natomiast do prognozowania generacji wykorzystywana jest metoda Holta Wintersa. Studium przypadku jest symulowane przy użyciu rzeczywistych danych mikrosieci dla wybranej lokalizacji geograficznej w Australii. Wyniki sugerują, że w przypadku prognozy obciążenia dnia następnego model SARIMA sprawował się relatywnie lepiej w porównaniu z metodami addytywnymi i multiplikatywnymi MLR, Holta-Wintersa; podczas gdy w przypadku prognozowania generacji, metoda Holt-Winters Additive Method i SARIMA dobrze sprawdzają się odpowiednio w okresie jesiennym i letnim. Wyniki sugerują, że zaproponowane podejście polegające na wykorzystaniu różnych modeli sezonowych do prognozowania obciążeń i generacji zapewnia wyższą dokładność w porównaniu z prognozowaniem konwencjonalnym.

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Trend analysis and SARIMA forecasting of mean maximum and mean minimum monthly temperature for the state of Kerala, India

Kabbilawsh P., Sathish Kumar D., Chithra N. R.

Acta Geophysica

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2020

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Vol. 68, no. 4

1161--1174

EN

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) fnding and flling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal diferencing and one frstorder diferencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-ft SARIMA models are developed for each of the fourteen variables. The study revealed that SARIMA(2, 1, 1)(1, 1, 1)12 as the ideal forecasting model for eight out of the fourteen time-series datasets.

4

Comparison of different approaches in traffic forecasting models for the D-200 highway in Turkey

Dogan E., Korkmaz E., Akgungor A. P.

Zeszyty Naukowe. Transport / Politechnika Śląska

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2018

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z. 99

25--42

EN

Short-term traffic estimations have a significant influence in terms of effectively controlling vehicle traffic. In this study, short-term traffic forecasting models have been developed based on different approaches. Seasonal autoregressive integrated moving average (SARIMA), artificial bee colony (ABC) and differential evolution (DE) algorithms are the techniques used in the optimization of models, which have been developed by using observation data for the D-200 highway in Turkey. 80% of the data were used for training, with the remaining data used for testing. The performances of the models were illustrated with mean absolute errors (MAEs), mean absolute percentage errors (MAPEs), the coefficient of determination (R2) and the root-mean-square errors (RMSEs). It is understood that all the models provided consistent and useful results when the developed models were compared with the statistical results. In the models created separately for two lanes, the R2 values of the models were calculated to be approximately 92% for the right lane, which is generally used by heavy vehicles, and 88% for the left lane, which is used by less traffic. Based on the MAE and RMSE values, the model developed by the ABC algorithm gave the lowest error and showed more effective performance than the other approaches. Thus, the ABC model showed that it is appropriate for use on other highways in Turkey.

5

Prognozowanie produkcji budowlano montażowej w województwie dolnośląskim. Część I

Rogalska M.

Budownictwo i Architektura

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2012

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Vol. 11, nr 2

121-137

PL

Artykuł jest częścią pierwszą cyklu „Prognozowanie produkcji budowlano montażowej w województwie dolnośląskim”. Założono, że wynagrodzenie pracowników będzie jedną ze zmiennych niezależnych do wyznaczenia wielkości produkcji. Prognozowano wynagrodzenia pracowników w sektorze budowlanym metodami regresji wielorakiej i metodą autoregresji średniej ruchomej SARIMA. Przeprowadzono analizę wyników obliczając błędy ME, MAE, MPE, MAPE oraz współczynniki Theila I, I2, I12, I22, I32. Sformułowano wnioski z obliczeń. Wyznaczono równanie regresji wielorakiej z 12 predyktorami wytypowanymi spośród 53 zmiennych niezależnych. Uzyskano dane prognozowane do predykcji produkcji budowlano montażowej.

EN

The article is the first part of the series „Prediction construction and assembly production in Lower Silesia.” It was assumed that salary of employees will be one of the independent variables to determine the volume of production. Salaries of employees was predicted, using multiple regression and autoregressive moving average SARIMA methods. An analysis of the results was carried out. The errors ME, MAE, MPE, MAPE and Theil coefficients I, I2, I12, I22, I32 were calculated. Multiple regression equation with 12 predictors was set. Predictors were selected from among the 53 independent variables. Forecasted data were obtained for construction and assembly production prediction.