Day-ahead load forecasting of integrated energy system based on transfer learning and ensemble learning

Abstract

As a new and efficient intelligent energy system, integrated energy system has been widely used. As more and more new buildings are incorporated into the system, accurate load forecasting is essential for the planning and operation of integrated energy systems. The historical data of new buildings incorporated into the energy management system is not enough to build accurate prediction models. Transfer learning, as a cross-domain learning method, has been applied in time series prediction. To solve the problem of negative transfer caused by fluctuation and randomness of load data, this paper presents a day-ahead power load forecasting model that combines transfer learning with ensemble learning. Firstly, a multivariate migration method based on data decomposition is proposed, which migrates the data of multiple buildings with high load similarity to enrich the historical data of the tar-get buildings and avoid negative transfer. Secondly, similar day and neural network integrated prediction models are presented to deal with the impact of different date types on prediction accuracy. Finally, the proposed model is validated by simulation experiments. The experimental results show that the proposed method achieves good prediction accuracy.