Multi-Step Load Demand Forecasting Using Neural Network

Dewangan, Chaman Lal; Verma, Nishchal K.

doi:10.1109/isap48318.2019.9065953

Cited by 2 publications

(2 citation statements)

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“…In this paper, effective time-lags [19], weather, time, and non-linear features are used for developing the model. The effective values of lags for PM 2.5 and PM 10 are found using the cross-correlation coefficients [20].…”

Section: Methodsmentioning

confidence: 99%

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Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

et al. 2021

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Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM 2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM 2.5 levels of LCSDs. The dataset used for the experimentation consists of PM 2.5 values and other parameters (PM 10 , temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM 2.5 , PM 10 , temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R 2 ) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

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Section: Methodsmentioning

confidence: 99%

“…6) Deep Neural Network (DNN): Artificial Neural Network is a widely used tool in various time series regression and forcasting problems [19], [27]. It approximates the non-linear relationship between inputs and output for developing the calibration model.…”

Section: B Techniques Used To Obtain the Calibration Modelmentioning

confidence: 99%

Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

et al. 2021

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Domain adaptation based deep calibration of low-cost PM2.5 sensors

Kumar¹,

Arora²,

Tripathi³

et al. 2021

Preprint

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<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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Multi-Step Load Demand Forecasting Using Neural Network

Cited by 2 publications

References 19 publications

Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

Domain Adaptation-Based Deep Calibration of Low-Cost PM₂.₅ Sensors

Domain adaptation based deep calibration of low-cost PM2.5 sensors

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