As the energy consumption in buildings accounts for about 30% of the total energy consumption, there is a growing need for people to pay attention to energy saving in buildings. From the perspective of building life cycle, buildings consume 70–90% of energy for their operation, and therefore it is very important to reduce the energy consumed for building operation. In this regard, we developed an Artificial Neural Network (ANN) model, which predicts when the heating system should run in order to reduce energy usage on winter mornings when the energy consumption is high. Especially, existing research about heating timing uses typical variables as input data of the ANN model. But in this study, accuracy of prediction is improved by adding time variable to the ANN model. Consequently, the predictive data of the ANN model were found to be significantly similar to the empirical data of BEMS, and the prediction performance of the ANN model was approximately 13.13% of CvRMSE and 0.197% of MBE, thus satisfying ASHERAE guideline 14. Therefore, the ANN model proposed in this study would help not only in reducing the energy consumed in buildings but also in providing pleasant thermal comfort by predicting the optimal heating timing.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction
- Safety, Risk, Reliability and Quality
- Mechanics of Materials