An Artificial Neural Network-Based Approach to Optimizing Energy Efficiency in Residential Buildings in Hot Summer and Cold Winter Regions

Resource depletion and ecological crisis have prompted human beings to reflect on the behavior patterns based on industrial civilization so as to seek ways of sustainable development of human society, economy, technology, and environment. The energy consumed in the construction process, commonly known as building energy consumption, accounts for more and more of the total social energy consumption, and with the continuous development of social economy and the improvement of living standards, this proportion will be larger and larger. The structure of the neural network directly determines its performance and work efficiency. The structure optimization of the neural network is not only a hot issue in this field but also an insurmountable key step in engineering applications. With the increase of network depth, the structural optimization difficulty index of the neural network increases, so solving this problem has important theoretical and practical significance for the design and application of the neural network. In this paper, the energy saving of buildings is optimized based on the optimization of structures such as particle swarm optimization (PSO) algorithm and restricted Boltzmann machine. The experimental results show that the BPNN optimized by the improved PSO algorithm is significantly better than the non-optimized BPNN and the BPNN optimized by the basic PSO algorithm. The comprehensive output rate of the optimized neural network can reach 64.5%. In general, the error rate of the optimized artificial neural network (ANN) will be 57.65% lower than the original one.