  Precise load forecasting in buildings could increase the bill savings
potential and facilitate optimized strategies for power generation planning.
With the rapid evolution of computer science, data-driven techniques, in
particular the Deep Learning models, have become a promising solution for the
load forecasting problem. These models have showed accurate forecasting
results; however, they need abundance amount of historical data to maintain the
performance. Considering the new buildings and buildings with low resolution
measuring equipment, it is difficult to get enough historical data from them,
leading to poor forecasting performance. In order to adapt Deep Learning models
for buildings with limited and scarce data, this paper proposes a
Building-to-Building Transfer Learning framework to overcome the problem and
enhance the performance of Deep Learning models. The transfer learning approach
was applied to a new technique known as Transformer model due to its efficacy
in capturing data trends. The performance of the algorithm was tested on a
large commercial building with limited data. The result showed that the
proposed approach improved the forecasting accuracy by 56.8% compared to the
case of conventional deep learning where training from scratch is used. The
paper also compared the proposed Transformer model to other sequential deep
learning models such as Long-short Term Memory (LSTM) and Recurrent Neural
Network (RNN). The accuracy of the transformer model outperformed other models
by reducing the root mean square error to 0.009, compared to LSTM with 0.011
and RNN with 0.051.
