Improving the accuracy of multi-step prediction of building energy consumption based on EEMD-PSO-Informer and long-time series

Accurate multi-step forecasting of building energy consumption is an essential tool for effective planning and plays a vital role in building energy management systems. With the advent of big data, many artificial intelligence techniques require long time series to predict multi-step energy consumption. However, energy consumption data of buildings are often nonlinear and non-stationary in the state as well as considerable period, making model prediction more difficult. In this research, we propose a hybrid algorithm that combines the ensemble empirical modal decomposition (EEMD) and informer, where the parameters of the informer are optimized by the particle swarm optimization algorithm (PSO). At the beginning of our improved method, we use EEMD to break down the raw data into several intrinsic mode functions(IMF) components. Informer is then used to make predictions, and PSO is used to tune hyper-parameters during prediction. In the final step, the final prediction result is obtained by combining the prediction results of all IMF components. The hourly electricity consumption of five buildings in the BDG2 dataset is used to evaluate the effectiveness of the proposed method. Five existing models are compared with this method and evaluated by different performance metrics. From the perspective of five cases, the accuracy rate has increased by up to 78.68% compared with the existing methods. Compared with the original model, the accuracy rate has increased by up to 56.11%.