Building energy consumption data plays an important role in building energy analysis and energy saving optimization. However, due to difficulties in collecting, high cost, equipment failure and other reasons, the coll...
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Building energy consumption data plays an important role in building energy analysis and energy saving optimization. However, due to difficulties in collecting, high cost, equipment failure and other reasons, the collected data are prone to be missing, which hinders the mining and analysis of building energy consumption data. In this paper, the bayesian network is used to check and recover the building energy consumption data. In the case that the amount of time series data missing is less than 50%, the method G-test is selected to identify abnormal data, and the Naive bayesian optimizing Expected Maximum algorithm is used to check the data. When a large number of building energy consumption data missing, the sparsebayesian learning algorithm is used to fill in the missing data based on the compressed sensing theory. The results show that the model can effectively deal with the problem of missing data of building energy consumption and can be widely used in practical projects.
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