Accurate energy consumption prediction is a prerequisite for effectively dispatching distributed power sources. For a building, due to the frequent fluctuations derived from many dynamic factors, the precise energy consumption prediction is still facing challenges. Existing methods usually only use common recurrent neural networks to predict building energy consumption, consider common recurrent neural networks model does not have the ability to extract spatial features and they have a long-term memory problem, so they have limitations to deal with long term task. To overcome these challenges, in this paper, we propose a hybrid model to predict the cooling consumption of a building.Our hybrid model has the merits of convolutional neural network and gated recurrent unit in capturing spatial-temporal features. Experiment results show that our hybrid model has the best performance, compared with other methods. The result will benefits managers to make reasonable scheduling of power and equipments.

Reducing carbon emissions from buildings is crucial to achieving global carbon neutrality targets. However, the building sector faces various challenges, such as low accuracy in forecasting, lacking effective methods of measurements and accounting in terms of energy consumption and emission reduction. Fortunately, relevant studies demonstrate that artificial intelligence (AI) and big data technologies could significantly increase the accuracy of building energy consumption prediction. The results can be used for building operation management to achieve emission reduction goals. For this, in this article, we overview the existing state-of-the-art methods on AI and big data for building energy conservation and low carbon. The capacity of machine learning technologies in the fields of energy conservation and environmental protection is also highlighted. In addition, we summarize the existing challenges and prospects for reference, e.g., in the future, accurate prediction of building energy consumption and reasonable planning of human behavior in buildings will become promising research directions.