Application of IEHO–BP neural network in forecasting building cooling and heating load

Abstract

Countering the issue of low optimization accuracy and poor stability of the Elephant Herding Optimization (EHO) algorithm when solving multi-dimensional nonlinear complex problems, putting forward an improved Elephant Herding Optimization (IEHO) algorithm. The algorithm improves the accuracy of EHO algorithm optimization by chaosing the initial solution, adding dynamic influence factors, Levy flight operators and boundary mutation operators in the position update process. Standard functions are used for test experiments, and the results indicate that the introduction of improved strategies can effectively improve the accuracy and stability of the EHO algorithm when solving optimization problems. In view of the performance of the IEHO algorithm in function optimization, combining it with the BP neural network, proposing the IEHO–BP neural network algorithm, and using new algorithm to forecasting the building cooling and heating load. The experimental results show that compared with other group intelligence optimization algorithms, the output results of the cooling and heating load forecasting model based on the IEHO–BP neural network algorithm are more accurate and less oscillating.

Keywords

• Load forecasting
• BP neural network
• Elephant herding optimization algorithm
• Weight and threshold optimization
• Swarm intelligence