Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 4

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: ensemble of predictors

Sortuj według:

Ogranicz wyniki do:

Bagging and boosting techniques in prediction of particulate matters

100%

Triana D. , Osowski S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2020

tom Vol. 68, nr 5

1207--1215

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.

Bagging and boosting techniques in prediction of particulate matters

88%

Triana D. , Osowski S. , Triana D. , Osowski S.

nr 5

Ensemble neural network approach for accurate load forecasting in a power system

75%

Siwek K. , Osowski S. , Szupiluk R.

tom Vol. 19, no 2

303-315

The paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

Local dynamic integration of ensemble of predictors in load forecasting

63%

Ciechulski T. , Osowski S.

Przegląd Elektrotechniczny

2017

tom R. 93, nr 10

57--59

The paper shows the new approach to integration of an ensemble of neural predictors in load forecasting. In opposite to classic integration method built upon weighted averaging of every single predictor results this integration method uses only the results of one predictor which was the best on the input data of the learning vectors from the past, which were closest to the actual excitation. Thanks to this the result of ensemble is never worse than the best unit in ensemble. The results of 24-hour ahead prediction of the daily load in small power system have confirmed the efficiency of the proposed solution.

Artykuł przedstawia nowe podejście do integracji zespołu predyktorów neuronowych w zadaniu prognozowania godzinnych obciążeń dobowych z wyprzedzeniem 24-godzinnym. W metodyce tej do predykcji używany jest tylko jeden – najlepszy predyktor dla analizowanej doby. Konkretny wektor obciążeń z danych uczących wraz z najbardziej dokładną odpowiadającą mu siecią neuronową wyłonioną w trybie uczenia wybierany jest na podstawie najmniejszej odległości euklidesowej badanego wektora w trybie testującym. Wyniki badań numerycznych potwierdzają wyższość prezentowanej metody nad rozwiązaniami klasycznymi predykcji.