Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors

Siwek, Krzysztof; Osowski, S.

doi:10.1016/j.engappai.2011.10.013

Cited by 92 publications

(52 citation statements)

References 21 publications

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“…As confirmed by several measurement surveys [15,16], PM10 concentration depends on weather conditions such as temperature, humidity, atmospheric pressure, and wind [17,18] as well as on natural and anthropic factors. The plurality of variables to be measured makes the evaluation of the cleaning state of HVAC systems rather complex, especially for monitoring purposes.…”

Section: Pm10 Measurements and Environmental Conditionsmentioning

confidence: 94%

Automatic and Noninvasive Indoor Air Quality Control in HVAC Systems

Basile¹,

Bruni

Buccolini

et al. 2016

Journal of Industrial Mathematics

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This paper presents a methodology for assessing and monitoring the cleaning state of a heating, ventilation, and air conditioning (HVAC) system of a building. It consists of a noninvasive method for measuring the amount of dust in the whole ventilation system, that is, the set of filters and air ducts. Specifically, it defines the minimum amount of measurements, their time table, locations, and acquisition conditions. The proposed method promotes early intervention on the system and it guarantees high indoor air quality and proper HVAC working conditions. The effectiveness of the method is proved by some experimental results on different study cases.

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Section: Pm10 Measurements and Environmental Conditionsmentioning

confidence: 94%

Automatic and Noninvasive Indoor Air Quality Control in HVAC Systems

Basile¹,

Bruni

Buccolini

et al. 2016

Journal of Industrial Mathematics

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“…And the decomposition can be made for multiple levels by successive decompositions of approximations. The decomposition levels will be stopped for which the standard deviation (S.D) of the approximation component is substantially less than the original signal [11]. This is achieved through the empirical relation shown below:…”

Section: Wavelet Transformmentioning

confidence: 99%

Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks

Babu¹,

Arulmozhivarman²

2013

Journal of Electrical Engineering and Technology

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-In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.

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“…Meteorological attributes including temperature, relative humidity, wind speed, wind direction, cloud cover and surface pressure, as daily average, were collected from https://www.ecmwf.int/. Wind speed and direction were merged through the following equation (Siwek et al, 2012). …”

Section: -1 the Used Datamentioning

confidence: 99%

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Shamsoddini

Aboodi

Karami

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

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Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors

Cited by 92 publications

References 21 publications

Automatic and Noninvasive Indoor Air Quality Control in HVAC Systems

Automatic and Noninvasive Indoor Air Quality Control in HVAC Systems

Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

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