The energy consumption of buildings has risen steadily in recent years. It is vital for the managers and owners of the building to manage the electric energy demand of the buildings. Forecasting electric energy consumption of the buildings will bring great profits, which is influenced by many factors that make it very difficult to provide an advanced forecasting. Recently, deep learning techniques are widely adopted to solve this problem. Deep neural network offers an excellent capability in handling complex non-linear relationships and competence in exploring regular patterns and uncertainties of consumption behaviors at the building level. In this paper, we propose a deep convolutional neural network based on ResNet for hour-ahead building load forecasting. In addition, we design a branch that integrates the temperature per hour into the forecasting branch. To enhance the learning capability of the model, an innovative feature fusion is presented. At last, sufficient ablation studies are conducted on the point forecasting, probabilistic forecasting, fusion method, and computation efficiency. The results show that the proposed model has the state-of-the-art performance, which reflects a promising prospect in application of the electricity market.

中文翻译：

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使用具有有效特征融合功能的深度神经网络进行建筑负荷预测

近年来，建筑物的能源消耗稳定增长。对于建筑物的管理者和所有者来说，管理建筑物的电能需求至关重要。预测建筑物的电能消耗将带来可观的利润，这受许多因素的影响，这使得很难提供高级预测。近来，深度学习技术被广泛采用以解决该问题。深度神经网络在处理复杂的非线性关系方面具有出色的能力，并具有探索建筑物层级消费行为规律和不确定性的能力。在本文中，我们提出了一个基于ResNet的深度卷积神经网络，用于提前小时的建筑负荷预测。此外，我们设计了一个分支，将每小时的温度集成到预测分支中。为了增强模型的学习能力，提出了一种创新的特征融合。最后，对点预测，概率预测，融合方法和计算效率进行了充分的消融研究。结果表明，所提出的模型具有最先进的性能，反映了在电力市场中的应用前景广阔。