The invention provides an existing old residential district low-carbon transformation potential rapid evaluation method in combination with a
convolutional neural network, and belongs to the technical field of building photovoltaic integration, and the method comprises the steps: firstly building a three-dimensional digital
city model; based on the three-dimensional digital
city model, calculating old residential district city morphological indexes, establishing a
building energy consumption
simulation model and a BIPV
simulation model, and performing numerical
simulation; according to a numerical simulation result, carbon emission conversion calculation of old
residential area BIPV low-carbon transformation is carried out; establishing an association
database of the urban morphological indexes of the old residential districts, the
building energy consumption, the BIPV potential and the building carbon emission; carrying out the training,
verification and testing of a CNN
algorithm, and judging the low-carbon transformation potential of the urban old residential district BIPV; compared with traditional energy-saving measures such as pipeline transformation and external wall heat preservation, the method for implementing BIPV transformation on the old
community is beneficial to improving the energy self-sufficiency capacity of a building, promoting transformation and upgrading of the old
community and remarkably reducing urban carbon emission.