A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Building thermal dynamics modeling with deep learning exploiting large residential smart thermostat dataset
In this paper, we present a deep learning approach to model building thermal dynamics with large-scale smart thermostat data collected from residential buildings. We developed a Long Short-Term Memory (LSTM) model as a baseline and compared it to a CNN-LSTM model to predict indoor air temperature in a multi-step time horizon in 164 buildings. The study showed that the proposed CNN-LSTM achieved an average of 0.26 °C Mean Absolute Error (MAE) for one-hour-ahead (12 future steps) predictions, which is over 6% of improvement comparing with the baseline. Furthermore, the results indicated that the CNN-LSTM models achieved more robust performance across different building characteristics, system configurations and locations, with a standard deviation reduction of 22%, proving the effectiveness and generalizability of the proposed approach.
Building thermal dynamics modeling with deep learning exploiting large residential smart thermostat dataset
In this paper, we present a deep learning approach to model building thermal dynamics with large-scale smart thermostat data collected from residential buildings. We developed a Long Short-Term Memory (LSTM) model as a baseline and compared it to a CNN-LSTM model to predict indoor air temperature in a multi-step time horizon in 164 buildings. The study showed that the proposed CNN-LSTM achieved an average of 0.26 °C Mean Absolute Error (MAE) for one-hour-ahead (12 future steps) predictions, which is over 6% of improvement comparing with the baseline. Furthermore, the results indicated that the CNN-LSTM models achieved more robust performance across different building characteristics, system configurations and locations, with a standard deviation reduction of 22%, proving the effectiveness and generalizability of the proposed approach.
Building thermal dynamics modeling with deep learning exploiting large residential smart thermostat dataset
Li H. (author) / Pinto G. (author) / Capozzoli A. (author) / Hong T. (author) / Li, H. / Pinto, G. / Capozzoli, A. / Hong, T.
2022-01-01
Conference paper
Electronic Resource
English
DDC:
690
Developing a residential occupancy schedule generator based on smart thermostat data
| Elsevier | 2024
| Taylor & Francis Verlag | 2022