Weather Prediction Algorithm Based on Historical Data Using Kalman Filter
Nikolajs Bogdanovs, Romualds Beļinskis, Aleksandrs Ipatovs

Description of the Technology

The predictive methodology is based on parameter refinement procedures and it is able to adjust the heating curve in order to obtain required supply temperature consistent to the actual demand for heating in room. Adjustment coefficients for modulating the heating curve have been refined by the test and error procedure. Overall results show the potential of energy savings increase by ~ 6-8% at significant decrease in room temperature deviation from settings. Based on this work, recommendations will be made for the building owners about the possibilities for exploiting heating engineering systems. The accuracy improving algorithm has been implemented using programming language Python.

Fig. 1. Application of the weather prediction algorithm.



Applications

The predictive methodology is based on parameter refinement procedures and it is able to adjust the heating curve in order to obtain required supply temperature consistent to the actual demand for heating in room. Adjustment coefficients for modulating the heating curve have been refined by the test and error procedure.



Advantages

Building owners will receive recommendations for more efficient use of heating systems.



Keywords

Kalman filter, weather prediction, Python

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