Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyperparameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi'a is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32 % to 1.38 %, and the coefficient of variation (CV) from 7.5 % to 1.2 %.