Adaptive building day-ahead load prediction method based on transfer learning

The invention discloses a self-adaptive building day-ahead load prediction method based on transfer learning, and relates to the technical field of building and environmental protection. The method comprises the following steps: S1, data acquisition and processing: dividing an original data set into a small data set of a target building and a big data set of a basic building group, and filling missing values of all the original data sets; s2, clustering energy consumption modes; s3, source domain data screening: screening a historical daily load curve of a load target building energy consumption mode, and respectively constructing a data migration training set and a model migration training set; s4, constructing a day-ahead load prediction model; and S5, adaptive model optimization: continuously adjusting model parameters by using Bayesian optimization to realize adaptive load prediction of the target building. According to the method, load prediction of the target building is realized through data migration