Forecasting of short term electric power consumption for different types buildings using improved transfer learning: A case study of primary school in China

Abstract

Accurate forecasting of electric power consumption is of great significance to the energy saving operation of buildings. However, in practice, few reliable historical data to be employed to forecast the electric power consumption of a building due to the non-availability of a complete system for monitoring of its energy consumption. In order to accurately predict the electric power consumption of a building with a small-size historical data sample, a model is proposed for forecasting daily electric power consumption using TrAdaBoost- Long short term memory (TAB-LSTM). Some new data is collected from the source building with the maximum mean dispersion (MMD), which is combined with the targeted data of the building to generate a training set. The weight of the training set is updated iteratively by transfer learning (TL) and LSTM to predict the daily electric power consumption. This approach is applied to the energy consumption data of a primary school located in the Northwest China. The experimental results show that the proposed TAB-LSTM approach could reduce the average error to 7.8%, and hence it can be used as an efficient tool for intelligent operation and maintenance of existing buildings.