Study on temperature prediction and early warning of building resistive electrical circuit fire: A mathematics model

• Proposed a novel method for building electrical fire temperature prediction. • Physical process and mathematics derivation of prediction were solved. • Temperature step-change is linear-corrected via cumulative variety. • Temperature probability normal dist matches actual values well. Building electrical circuit temperature perception and prediction are crucial for preventing the building electrical fire risks. Nevertheless, existing engineering prediction methods often lack sufficient accuracy and timeliness by virtue of the fixed threshold fire warning or failure to predict the temperature step-change. In this study, a novel mathematics method was proposed to dynamically predict the building electrical circuit temperature. First, the Long Short-Term Memory (LSTM) neural network was selected as the basic predictive framework. Two types of three-phase electrical overload experiments (short term single-overloading and long term periodic-overloading) were implemented to establish the training dataset (temperature, voltage, current, and residual current) which were distinguished by sampling conditions and frequencies Second, a cross model was built to validate the prediction accuracy with conditions transformation. Third, the temperature step-change was calibrated via the linear relationship with accumulated temperature variation and the usage of complex structure LSTM. Subsequently, the second-order temperature residual as well as its normality test were calculated. Finally, the temperature probability distribution was expressed via the first-order Taylor expansion of the temperature residual. The results indicated that the basic model is able to predict both the high and low frequency temperature tendency. The temperature probability distribution interval accurately covers the actual temperature variation. The comparison with temperature probability quantiles and actual value enables the fire risk of building electrical circuit. This study illuminated the step-change of electrical temperature and dynamical prediction in building electrical fire safety.