Method and system for predicting building energy consumption based on depth reinforcement learning

Abstract

The invention discloses a building energy consumption prediction method and system based on depth reinforcement learning, which comprises: collecting building energy consumption historical data, simultaneously collecting building area, building permanent population quantity, building permanent population consumption level and weather condition data of building location. The collected data samplesare grouped and input into the deep reinforcement learning network model according to the obtained training samples to train and save the network model to optimize the state action value function. Finally, the prediction samples are inputted into the deep reinforcement learning network model to predict building energy consumption. The invention adopts the method of combining the convolution neuralnetwork in the depth learning and the Q learning in the reinforcement learning to realize the energy consumption prediction of the building, Compared with the traditional prediction method, the deepreinforcement learning network based on convolution neural network and Q learning algorithm can reduce the amount of data, reduce the storage requirements of data, improve the efficiency of data use and speed up the efficiency of data processing.

Description

technical field [0001] The invention relates to the technical field of building energy consumption prediction, in particular to a method and system for building energy consumption prediction based on deep reinforcement learning. Background technique [0002] With the continuous growth of human demand for energy, energy issues have become increasingly prominent. In the construction industry, reducing the comprehensive energy consumption of buildings and improving the efficiency of building energy use has become a research hotspot in today's social development. Macro-evaluation and analysis of building system energy consumption, and then the establishment of predictable building energy consumption models are regarded as an important means to achieve building energy conservation. [0003] Reinforcement learning is a kind of learning from environment mapping to action, the purpose is to make the agent obtain the largest cumulative reward in the process of interacting with the e...

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Problems solved by technology

[0005] At present, there is no relevant literature on the application o

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