Weather Prediction Algorithm Based on Historical Data Using Kalman Filter

Bogdanovs, Nikolajs; Bistrovs, Vadims; Pētersons, Ernests; Ipatovs, Aleksandrs; Beļinskis, Romualds

doi:10.1109/rtuwo.2018.8587795

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Control system developed by Python can regulate the curve of building heating based on the external temperature forecast. The idea of the developed management logic consists in adjusting the heating curve depending on a future state [13]- [15]. The research describes the shifted profile of external temperature, and the changed curve of heating is calculated according to a set of parameter settings for the whole heating season.…”

Section: Practical Implementation Of the Weather Prediction Algorithmmentioning

confidence: 99%

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Bogdanovs

Beļinskis

Bistrovs

et al. 2021

Latvian Journal of Physics and Technical Sciences

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The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.

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Section: Practical Implementation Of the Weather Prediction Algorithmmentioning

confidence: 99%

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Bogdanovs

Beļinskis

Bistrovs

et al. 2021

Latvian Journal of Physics and Technical Sciences

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“…To predict weather changes more accurately, mathematical statistics methods [4] temperature data to obtain the prediction formula. For example, Bogdanovs et al [6] used the Kalman filter to correct temperature prediction errors, which improved the accuracy of short-term temperature prediction. However, mathematical statistics methods only analyze temperature factors, ignoring the impact of other meteorological factors on the results.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above issues, in this research, we consider the relationships between temperature and various meteorological factors, and the spatial-temporal features of meteorological data. We combine Bidirectional Long Short-Term Memory (BiLSTM) [6] containing historical and future time-series characteristics with a Graph Convolutional Network (GCN), which can extract spatial meteorological characteristics to construct a GCN-BiLSTM temperature prediction model.…”

Section: Introductionmentioning

confidence: 99%

Temperature Prediction of Chinese Cities Based on GCN-BiLSTM

Miao

Yu²,

Pang³

et al. 2022

Applied Sciences

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Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperature prediction research, this research proposes a temperature prediction model based on the Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) and studies the influence of temperature time-series characteristics, urban spatial location, and other meteorological factors on temperature change in the study area. In this research, multi-meteorological influencing factors and temperature time-series characteristics are used instead of single time-series temperature as influencing factors to improve the time dimension of the input data through time-sliding windows. Meanwhile, considering the influence of meteorological factors in the surrounding area on the temperature change in the study area, we use GCN to extract the urban geospatial location features. The experimental results demonstrate that our model outperforms other models and has the smallest root mean squared error (RMSE) and mean absolute error (MAE) in the following 14-day and multi-region temperature forecasts. It has higher accuracy in areas with stable temperature fluctuations and small temperature differences than in baseline models.

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Distributed Temporal Data Prediction Model for Wireless Sensor Network

Gupta

Sinha

2021

Wireless Pers Commun

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Weather Prediction Algorithm Based on Historical Data Using Kalman Filter

Cited by 6 publications

References 5 publications

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

Temperature Prediction of Chinese Cities Based on GCN-BiLSTM

Distributed Temporal Data Prediction Model for Wireless Sensor Network

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