Models of particulate matter concentration forecasting based on artificial neural networks

Oprea, Mihaela; Popescu, Marian; Dragomir, Elia Georgiana; Mihalache, Sanda Florentina

doi:10.1109/idaacs.2017.8095195

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Introductionmentioning

confidence: 99%

“…Efforts to deal with high PM content are being actively made not only in terms of revamping the national policy but also from academic aspects [12][13][14][15][16][17][18][19][20]. In Reference [12], studies on predicting particulate matter with diameters that are generally 10 microns or less (PM10) using the artificial neural network (ANN) technique were conducted. Some studies predicted and monitored the status of PM by combining artificial intelligence (AI) techniques, such as the multi-layer perceptron (MLP) technique, with an autoregressive integrated moving average (ARIMA), which is a statistical model [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference

Ryu

Kim

2020

Energies

View full text Add to dashboard Cite

The increase in ambient particulate matter (PM) is affecting not only our daily life but also various industries. To cope with the issue of PM, which has been detrimental to the population of megacities, an advanced demand response (DR) program is established by Korea Power Exchange (KPX) to supplement existing policies in Korea. Ironically, however, DR programs have been launched hurriedly, creating problems for several stakeholders such as local governments, market operators, and DR customers. As an alternative, a method for predicting and categorizing the PM through deep learning and fuzzy inference is suggested in this study. The simulation results based on Seoul data show that the proposed model can overcome the problems related to current DR programs and policy loopholes and can provide improvements for some stakeholders. However, the proposed model also has some limitations, which require an in-depth policy consideration or an incentive system for power generation companies.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference

Ryu

Kim

2020

Energies

View full text Add to dashboard Cite

show abstract

“…No understanding of the mechanism of pollution creation is needed. Many different solutions to this problem have been proposed in the past [5][6][7][8][9][10][11][12][13]. They include multilayer perceptron (MLP), radial basis function (RBF), Support Vector Machine as well as Elman network.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Triana

Osowski

2020

View full text Add to dashboard Cite

The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.

show abstract