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.

翻译：近几十年来，随着物联网（IoT）设备的增长以及更多能源数据的可用性，建筑能耗预测与管理变得日益重要。然而，能源数据通常从多个来源采集，可能存在不完整或不一致的问题，这会阻碍能源系统的准确预测与管理，并限制数据在决策与研究中的实用性。为解决此问题，过往研究聚焦于填补能源数据中的缺失片段，包括随机缺失与连续缺失。该领域的主要挑战之一在于缺乏基于包含多种建筑类型与电表类型的基准数据集的验证，使得难以准确评估不同插补方法的性能。另一挑战在于针对能源数据缺失片段的先进插补方法应用不足。当代图像修复方法（如部分卷积PConv）已广泛应用于计算机视觉领域，并证明其处理复杂缺失模式的有效性。为探究基于图像的深度学习方法能否使能源数据插补受益，本研究将PConv、卷积神经网络（CNN）与周持久性方法进行对比，采用全球最大的公开整栋建筑能耗数据集（包含全球1479个电表数据）作为基准。结果表明，与基于原始时间序列的CNN（1D-CNN）及周持久性方法相比，采用二维重构能耗数据的神经网络模型将均方误差（MSE）降低了10%至30%。而先进深度学习方法PConv相较于2D-CNN进一步将MSE降低了20-30%，在所有模型中表现最优。