  Building energy prediction and management has become increasingly important
in recent decades, driven by the growth of Internet of Things (IoT) devices and
the availability of more energy data. However, energy data is often collected
from multiple sources and can be incomplete or inconsistent, which can hinder
accurate predictions and management of energy systems and limit the usefulness
of the data for decision-making and research. To address this issue, past
studies have focused on imputing missing gaps in energy data, including random
and continuous gaps. One of the main challenges in this area is the lack of
validation on a benchmark dataset with various building and meter types, making
it difficult to accurately evaluate the performance of different imputation
methods. Another challenge is the lack of application of state-of-the-art
imputation methods for missing gaps in energy data. Contemporary
image-inpainting methods, such as Partial Convolution (PConv), have been widely
used in the computer vision domain and have demonstrated their effectiveness in
dealing with complex missing patterns. To study whether energy data imputation
can benefit from the image-based deep learning method, this study compared
PConv, Convolutional neural networks (CNNs), and weekly persistence method
using one of the biggest publicly available whole building energy datasets,
consisting of 1479 power meters worldwide, as the benchmark. The results show
that, compared to the CNN with the raw time series (1D-CNN) and the weekly
persistence method, neural network models with reshaped energy data with two
dimensions reduced the Mean Squared Error (MSE) by 10% to 30%. The advanced
deep learning method, Partial convolution (PConv), has further reduced the MSE
by 20-30% than 2D-CNN and stands out among all models.
