Prediction method of energy consumption for high building based on LMBP neural network

Abstract

With the power consumption of high-rise buildings rising year by year, energy consumption of high-rise buildings has become the main object of energy-saving supervision and transformation, and its prediction has become a research hotspot. Aiming at the problem that the existing total energy consumption forecasting methods cannot accurately distinguish the consumption direction of high-rise building and the forecasting accuracy is low. LM algorithm is used to improve the standard BP neural network, and a prediction model of lighting energy consumption for high-rise buildings based on LMBP neural network is established. According to the prediction results of lighting energy consumption, hourly average outdoor temperature, hourly average outdoor relative humidity, weather characteristics, holidays, hourly average wind speed and 24 hourly hours a day, the energy consumption of air conditioning, power consumption and special energy consumption can be predicted separately. The experimental results show that the MAE and RMSE predicted by the design model for air conditioning energy consumption, power energy consumption and special energy consumption are 5.4707 and 8.6575, 1.7916 and 2.9356, 1.6075 and 2.2178 respectively, which are smaller than the comparison model MAE and RMSE, which proves that the design model can more accurately and effectively predict the sub item energy consumption in the energy consumption of high-rise buildings.